{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning example using Keras and Mobilenet# \n",
    "In this notebook I shall show you an example of using Mobilenet to classify images of dogs. I will then show you an example when it subtly misclassifies a bluetit. I will then retrain Mobilenet and employ transfer learning such that it can correctly classify the same input image.  Only two classifiers are employed. But this can be extended to as many as you want, limited to the amount of hardware and time you have available."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets load the necessary packages and libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.layers.core import Dense, Activation\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.preprocessing import image\n",
    "from keras.models import Model\n",
    "from keras.applications import imagenet_utils\n",
    "from keras.layers import Dense,GlobalAveragePooling2D\n",
    "from keras.applications import MobileNet\n",
    "from keras.applications.mobilenet import preprocess_input\n",
    "import numpy as np\n",
    "from IPython.display import Image\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall be using Mobilenet as it is lightweight\n",
    "<img src=\"files/MobileNet architecture.png\">\n",
    "\n",
    "\n",
    "It is also very low maintence.\n",
    "<img src=\"files/mobilenet_v1.png\">\n",
    "\n",
    "Source paper located here: https://arxiv.org/pdf/1704.04861.pdf\n",
    "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision\n",
    "Applications, Howard et al, 2017\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mobile = keras.applications.mobilenet.MobileNet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_image(file):\n",
    "    img_path = ''\n",
    "    img = image.load_img(img_path + file, target_size=(224, 224))\n",
    "    img_array = image.img_to_array(img)\n",
    "    img_array_expanded_dims = np.expand_dims(img_array, axis=0)\n",
    "    return keras.applications.mobilenet.preprocess_input(img_array_expanded_dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets try some tests on images of different breed of dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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RYU/qy4P9dZ/UU7nMESrI6qCYydoY4GQD763W62GpufQdTQVVOpq6dTcoDg5RhgMP/uA/XXPI6hesmnt1J6vxEUhJ/i+vf7ay9BvT7RTqlptQHML03Uj0sAomgUCPaC2MMxx/lq2epmrBunhiERxvhDHOe2QfHvjQCstwmkS4ztIWCY9vGMke50LZJ4VeVRhfUD8DB4GuOEfeK6a5jPWVCQ0084kjlpm+WRe+zB4Y/wCuhlku1RCzKHD0xlO6MnK7MZOPY68h5qH0p6aoUrWqQFOD278e3HIOmajorbW081RQU7UbklJEA+RznJOO3sdBNKKIlLHmKN+qmmq4HMhqqV0/dEk704z/ALGqMazxU8VTBIxO4EFTkD20dpqI3FKuMOomd2MLheAe3zfTX1LNHDbVdqySfKLmKNCoSQcYb6A6KGAFSdjxPlNfbnYaGCtt1bPT3BKkSRzxuRJHKBwynwQD3040/wCL91uNNb5b81DfJRFJTySXqgir32FR8oMqnaDjxgnznSRW+lPRmk9QIyuJFUHOcDB1FTUkBpIxMxSVmHbnC/X66oVQ0SN5IJAj7B1J03W0Fvgi/D6zVddbp3nBWoejWSEPuWJkjBEo3E7mcH5TsVVXRCRPwkq5HqKqnrqOeYl5KekpaoQxMeSkf7z8gPA+gGlDp9omrt6MqzKCEOcMxwe36aiag6ekZnkiXexy3z+T+ugEatrI+hP+ZKtD0lwaonKh/Uddqs+CsYx4wfP11dqklrKSYzCL11lDFmYtIyAcAAcAfTvoTSwi9U0JuLrUnnEiDk48/f66+SFbN6rUlJI1SCWy4Y7x57/T21buMDUXUnzBs01eXkRZNlIrEoqRAb/YZOpqW5VSYml3VSwqFkQkY5z+p18ller9IJII1b94qKQcHHb3Gqaxb8mRTt3Y3Z+bd7lfA+ujalIppJriXrHKprpVgpo4IzyS5Y/LjO0Y0QuNpqoZBXU1MIWChlmiJ7n3U6HNCa9i1E7F4hjC8Y+vHnUFHLcqWrd2rJG3KRIpYc4840E3q1D8pFkG57uNxjCS/wDTvLVPHzKf4W9tp0vUlRUzTsY6f4ed2w7PFlMfQaM3asW5PJTVtOYKlFyHikwD9T7aVbnca+yCJg7+tIzLz8+cfXTWFR4Et5sQ5JPUCnnSavVRt/IYgpb6Zxk6mtdTDT26nlQS+sRjfv4OG7aRluNZXHMkcjNK20F+CSeOB7aL3SCagpIKO4o9JlRGxBzj3/XR2x1Qlr95oNLW0ygzMAVwdzLzoPdqSe83FcI8KxIAsRkyHOPzfQ6A2xqakC0bTOTPCXiYg8Y8Y9vOjUdTUzStMryRyjkMgyoH+nGkigRyyypAO80j8PIRa/w0qq1U9KrvlTIogdQf+ngbaxB8/OTwfbOhF1oIKyCSWi2ht29AoBOD+YH6ggfyzrQF6WC/h301aqpnpK2C3xzQyrJjEjkyMfv8xz9/ONK1J01U0avUSySRrMMSZTaEkydrcdgfccZ+mtTBsl+82lSgF9hMoq0M1weRyRPgB9o8e+B4xznxoHX1t0td7ir6aRlraf8AewSRjkFDvGP1/mDpv6kt7w3BtzbZYxw8Y75yeQPr9BpfrhOIVLtHJMjfMJEwxUgruHHPc8dxpnkQbLRmr9TzUHW1lp+tbcI6b45Sl1gifHo1iDiQDwHXn76zumWmnrUSsu8CySKGZ5AWIQjyMcnGr/4OX2GluFR05dpVWju9KafDDOyRcGOQe+Bu18u3T13tt+kMdhqXqaZPh2Vpcj1B/Gm0D5T3AOsl0GFzEupFVkHnn6/7gy609HU11ro7NRyTUkTs1VUxwtG0gJ4LA9tv9jova7TBFUOrsklL8wU7sHPhdW6OwdXXW9fEPa5re0joJfiicEBcEYPvjXt+mqijq6xKmWKKSMMwQxkEtnsCfHt51TJnGQ0KHyET1AiL09C0k7iOnaRUbmNUyvtxpgtoW1tvanjjmZcKAuR27bu/6DUNDFUUm+WVp3LE+gsZBHbkka8pfJIaSop5gJMfOo+VTnOhP6hL/KT2XpiW6Wi4yU9RQQQ04Z5jUSiKR377VzydKt+R2TMiu7bQu89hx4OmqCdK+GKKpj3I/IQruwRyM6FdVRtR21SKHcA+Fm3naB7EZ1OIsH3kqaMBWK501EtRQVSQNVVbRRU7vnKktgjvjnOdPS/hZJc+vLbZKicpQVDlqqVSPliQjeM+5B2j7jWV0lqq654jTqN6urtwCQAQR37YxrpGruq2vpW5dVmsklro6L4eNmQAGVmyQF4BJGD+mm22fY8x3Ciux1TGfxKvg6u/EUyUzM0CztBTrvC7YUJwM/ZTz9cahBp4KgjeZRGwTeRjef4toJ7Z/X7aWKA7K2GeXazxwFyT8u588jOeRluftq9SRLX1kTNtqJRwoiHAA8D6fX+utAKAKhwdTXNOstTJWyuaRlikUkb8528+Nvf9PbWt9O2b9nUKLUMWjARdrY+bADMT75Os76MtsC0kKTgxllABJ5Vec49s/wCutLig/ZtFHLgsoUemnYEn6+3OlS2+00QnpjVaL5Rz1stHK7Evj1xx8ibf74J48fy1zC9iprP15U08DJGkdRPufGHJAJXP6Y41rfS95poOoa0mJpJXkZXfB2qxHGB58fc/TSR13QwUH4g1U1UwBrKeOaJWkHPBXx5BHP11TMNDBveZ/UfgB9j/AHgG3Mk9raatp9yurElT2x7+w0PjpaStWCFZ1/OBH/5Z7ZPvnRCO4U9Fb7hbqsPHI0ZEBAyrHyMjtnvpXSnnWaONX2hEV42A7keNDYA0bixAIG8h6ipqS3tLFU0jJUK5+cMeD5IA8/31L0hUyFAsNTJiIflRS28A9z7apTyfGzPUVHrNUyy7izDHIOMgaN0V+qYqeSlgjpoopAQ7xxhHwTySf6aFk2WhvFXWuJYoAheZJHWNpcFQp4PPJH276WeoxVwXBjDJ6eIwJCJMiTH8X66M1VsrbbT1dWaKqkigIU4iO1AfG7GORjU1o6Wu/WgrIOl7HUXKamjMsyIwMkUZ53kH+EapjPquRYAswdBQVM9G1UiriCHfJ84DHn286vx0CywxPITCA4EoJ5ZT/nnVXbGrRR+oTUQFom2kYB7c4+urcU4jqfRqf30ksWwrnC4J4OfBBGudj4hWqUUsdSlVVfDFSRHvRpZdnAPBUnjOPA0zp0XUSIr/AAFM24A5Myc6c6fpyi6r6Pp/XqKN6iOOam9KruCU5hmAGwgHGPse/jVKl6cpaWlggqLT09LNFGqO7XU5ZgMEnDe+kv6pDsTREATM+p7w0MQjgxuPymTZyTjJJGmq10b3DpusjW4erPHMirCRk7cZyD3x441Vq+lKtqBZ4oRieQBBGfmUnsNV7fbbpSFVMka0jEh334bPbTvcxtuJIYXxKEVXV2siOpMXrHO0YGSoPjyNDjXNJWehGZF353tv/iPk51q1nFpoKF0q7ckzyRen6zHLkd+D99DbfYqKOGWpFFCBHJvEjgn5QOc/TS/fQXYlGqri5Z7Vc5blHTsrRmQA08zodzkeMDQy4mOmu2yYmGb1fSk3/LhicHPsNbNQ3uluklPNb7bBSybfTQLhAOfc8j6keNZF+JlupqXrKvpaaQmJZgzFW3KHKgkA+RnV+lzd9mU7EQ2Eq9ieLlSUFbJ8RTViSmJ/RdTwc44P1HHfQGj6Ou98lijjirYY1YyLIIWCFfJ3dscatdNUMVx6nstDK+xKurWn35ztLHAP89dq2foSostqqKOarSrjhpmLrvx+6A5Hfgd9D6nqW6NlRNyYPKDiIAnD1V0S1bfUounxUz1c0i4iYkKckDvjgZyftrTLr+FnUtZ01cRW2yOgkpQNscqH5iowWDdscca2akjt1q6/tP7LgjjgFueKSWaUlk3MpHB+3H31pyKlRSSLVNE9M6YOHyOPppTL8RagQLlQQ+5nHFL+GNXdLt03X3GVKmgkp/SqJ41KoshG1Yz/AOWcaZa/8FOpbLGTXW146MMiyky4aNS3HGOeO+ug6yo6apqVWhp4q1qZ/mVFGN5Pcgef9NXrjfLZ1LRRfs+uDSVGBh1bKle4PHHbSq/EXytpr/EmhMq6tfCJFSSMKLCiEhuI+20FuRtP1x414pad1t4hqy+FAZwqlc5848/66HfiZLJ6W30oY3kQoGjJAOe6svkeQQc9x7a99KV5moVpY3X1RDvjkySFU4GAT3xr1qkrjE9MtM5ib1pZDLK89MsRGzBWVCd49i2eD/Q6yyrlgoUNOYZ6dS3KO5kiyOPOSp9iNdC3aiGZHjAjXaSXUnBJ/h+v0+2s36loo5RK8g9RVwXIQY9jkfy0XG54kZsQq5jTwNS3KlqaeVhEZA0cgIynuMj/AHnXbHQMlP1RHa69/SmNwp8ASHLCoU4OCMYyM8fTXHE1nqVafbG7Ue5gyKmWiHhv0IH9ddF/8OizVtjeI1KrNHWf9Pl+w2+oGHsDj+WlPiOIZsBHmIri7qtj9/7jibxcrLGVmSrqYo6ll9MeogDYP+EnvjXL3W/xsV1q6OBDcJVqW9BmGPVXgDt5+2ur6q7U3UVBT3OgheWqSMbEyPl452e+TwT7axGpt1VQ9dWWpu9Cwws9WJGDFThtxXbjsO2vH9GVwZG3s/4nnwNB3ipY+ijNahV1qzyemxiqFSTBj4JDA+Dnj9NOlL+B9omttJW/D1e6WUE+oTgqSM4476sWeJl/bdRXUwit1aI6h42PKI8hKtn/AHxp0v8AZupqmxNJ0j1dXUlXI0bRU7JG0KIDzt4znHbXdT1GQHZ6+85iTzKFv/A3p2ib1KinnccfuzM2C399L/459DWmzfhdc5LdTxW9oZ4XVVB3OA2COT9c51k3XF56+gvMVD1D1ZMqSZWaWiOCDgnmPjJBXwfOs+k6hr7r05eP2/cbhX0KBVpVllLR+spGHyRnOM5XxnTHTdPmyOuV3sAj395CVqBjv/w+z01be+o4ayCKoxZ2aKF1XDESLzk+wOdMn49037H/AA/slpjPptPUJLMqdt4PC/yJ1mf4HCoPWNvhoqZaqescU5UtjCkhmIP/AMQdbF/xFWSoF1tVuqUEkbSBkkPZowDjJHbjH8tehRdHVFjwanoOnxkoz+9Tml7e0ksADZVkBanXyPAJ7ADkn760LpmxyRD5YAV+Xa8YVQPp/TU56dlmkpTReiuVxLKRtDY4J/8AiO39AO51foGMF/p7PTBgYoBLUSHueQAB7A5++tF2sQ+JKaPXTMLQIkVVF6ZZid6oWyAf8XYZ9tF+qq8rZakU0bF1j+RQcHOR2/vr5QlvRp4KZHdTnDqM5PjOhvVc70ltg3RgyTZUIW4Z+Qq/Q5I0ops1NMjaLHSk71t/hRRmkjqlJdC2WJBACj3PA+gOdWvx/Kx9RWWGnSOKeKgdArsFIG8YA+xz/PVW00dbR3+y09prCZnUv6jLkoFzl/bG7Ix5A0D/AOLi0yw3zpesBEkP7JKLKxw7v6hySPHfTbLrIBmRmHoIiO13ExlgjnCliuEYHcw/i/kdSXa5iiqqNZ6iCkpmjO5jk8e4Pg+2kromB/2nJJIGdUi7N55+utktf4SR9fsKu6SzU1thpiivEpBiY8hiezc4G3+ulcvawfjO0UOlMdmKdBeLSLjT09bumpiu1nafneRlSCOf008dG22rqLvBa6Pp+WqkvZFPHTPsEgG7JlG78oAPfQaj6Mt/RFda6Crntd4NXVhUihp3FVUgeRMpPp/cDHvq/c71WUvTUsMVvq7DBNWO1ylp5lkaBUPyRCT8wZ/P89ZOTMmRguPcXV/vWxq/IB9oq3G0bbh1LHS01y6b9W4JFRVJgq4allaOQK3yhu2SDkZ850yWfq2KxdLyVNrtcVsraQyhZ1ba8sXOQx74OeFOdZy9nt1YE6irYKiptjwLNU009XljU87yxAyc4Bx7g6B3uvFT8fPJXUz1MoCvTLGeVAwpU/lxjH10u2FHNISKI8H+f4nBbnuX8QrzeQLdUNCKDI/cQ0iRR7SdwZ2UZb+edWZIaaJYqquKPHUjbnlkB9y3gdudLXSpd53R45TAowxRd2Hzwfpga0gBY6IwiHgxqwTnGTwW/lp3K2ggASuoKaiTXWNbvdo6GGkpHRnCB4JQc+3B76GTXipp5pIf2LKfTYrnYPBx7afrbYLdFcamS6UNLBHPTvDUSs7PLETyjR4wBjv/APrRaO7dIRRoksPqSKArO1ICWI7knPJ0S0IBqWZgTtNBWhqKCzD4EUszzErGjIhFOpGMnPc6zyTpGQSVFNXSsrxoofBA9VSc5x2BHfUC9SSE/FmoLyF/TMTHcF7YO7316q7oax1nr5QCjkbAwyxHIz7azceLIlkR0Y1u5PS0zJO0Lyp6cQ/dgvkyKPH0POvdTUPS2+aCRfhtlR6Y+fJK99uO3OO+hBq0pSahYmczyYyG+TH31DfZZK2J2SVA5/Mz8sCe5A88aq6MSLMVfCzXRny532kobhIYZ/iQpBJThQx7gE9/bjSTeJlqamOochZJf4Qc+eNW56SW61oihh/6eFFVcALuAGMnQmrohDLAwXlCVfAxk++PGtHo8aJlJHNSuDEEcmV6ese13KkrI3wKWrinHbgq4JP9Ndk9Y9UUVJSm5OFnWoKQAE44c8cDOT9NcYVsYmU+mqFHQ/MO+RrSKXqJ7703aYmqFiWjKuiBsFJF4JyfOlvi3Sf1Hbf2sQudNQE64Sltc9siuE9BSCV4SsbugyQB35x/I6RLJ1vQ03UcUNWsCUZY0sRKqiu3cl2JwvtrGIOrKoVUk90nqHhg5jUy5Unzhex0hdRXdqydXyZIhOJmwuRk8gYPjWFg+Fs7G3ibY63nVtx636ftF6+BqZYoaaSRpZHYMUhYDftyAd2VxwNM9m65tc9iikqZaD4yQExxU5OHQnCsoYA9tcVV/Vcs08a1Db8LtPylmlPvk/l4440wWzrivWEpJIYP3qmJy4YxL5UN3AOO3bR8nwvIigq28ppN7TUPxltL0/VFLPFkQVGxZYZJNiE/4lPYfr/TQ/pil/ZFXLb6ogPAMwvnkpnOPv308/iRFT9SWez1NBKuyopoZYl3AkErnOe2O4P9tZ1U1HpV1I0h+GqYxioQZwxBwSc9uOfY5zr3DHUk9PgFNHWtpJJIZXhUSrz6qhsFR4J8ayzqKCGnvFCa2WeOgnYwzMg3PGxGEcL/ABc8EeR9sHcLL8NV2556hh6aLmTLAZx4J8/prMOvrVSXONvSZY6edGdHDYxjkMvtt758EaCtjePOoZSJn1Ha3oKuoikZZKSRSdySb0IwSChPYHvgnyRjWpfhjNS2u7TU+6Kj+IjikznCqMEkj67c6yGrlqKSJ6ytqAtSyPDUK4wu85Vs47Z7kY8586PdH2m89a3Cmp7URI/qKisr5JGzZ3BH1PfnVzj7oPtM8PoavM3T8Pep7fSTdT0c1buNFW4pHji3epGxYsd2ewY41H1J1DUXG8iRI80IpTTCUSfONzfMQPHHGm6k/wCF2SLp+drTcDSXj4c+jESfTLA52ls5wceeQcaw/rLpXrzo65ta7haa1ZEOVmhjMsUo4+ZXAwRz75HY68p1nRP39eIbH+fzaYnVYXOQ6Rsd9o2icRyVskVTTrG6LGsDkbdiA7cjP11kN9/Fe82aJ7bT3CGqpZZiHizvRDu8MuD9ufGpZ+gOs7lNHVVdPcfhpk4Vadv3i/TgaGx/g7eKpo0goallrH/c76aQMgDYIIxweR3xq3T/AA8arznUPaon2XB3ES7pXx11VKK71KlmL4qmizIc855PuOOeNBKennjpTTU3xJM4PrJJkKuDwwwcEn7a6Jtn/CL1XeJMSRNRytErBmOBE5I4OTzxptp/+C2/0UckYvNPU+q+1R6uNi4/OfoD7e+vShVC+kRsdM9cTBvwziuVlxdKKCZpqKUfvIlJ2PuG3t5+nkE60vqz8TF6pqVt/UFL6NTSs/pFuyApuGfO0lWH6jXaH4efhJ03+GvSdPY7NTR1EpYTVVZON71FRtCtLz284A7fqdZd+Ln/AA7WK+00tZ0/QQxV7b3dncpG3HzM+PzE4+2P56n0695r4iVxhZzlaa1MUr00pBjiSFcxh1Mn2A+ZsknHbIJPYAj7BaqipuklxrqqNWqslfUfD+mrnBIPP6+SdC7Qtf0jdWpLlRzTL6/yb2ChQe7DPjsAeOOdPNDFSXs/FBhLBEg9SpKnZO2eEizgqi8DPnk+dEyKV28RjAQWuP3T9ugg+eOMSlhjIkBUlRjtnx/lpS/ECsX14YJeZIs/l5O7AA5+nP66a7NTpbqVo6aADYVSQhQPmPn6Dn9f00n3akJqayuq490m3KJ43HgHH0GT9Tz40HYGaLj0zz+H1I8nUIqqinYEDOS+2KFFXAUjuTk5x9APOlf/AImLXLf77YKiiUwWSlpmpFqCxcNKZMnPhe47cajq+t6/pSCS3UlNFVXSuYtvbhKaM4Ck+574xz9RronqLpGy2Dorp9rrQUlbTR0okaCSJnLOwHzMT3OTnjjRtdEEnaZOUCjc4ooOlZun7lLDNI0ucK7gZUEexyRpiqbt1JUXekjo7xVUtDGg+QKdgKnOVUcFuMfTW9R9HWKhhrf2VSqs1cyrHAj70Qj+HzkYOffQrqlrQtuphTxioraSZpCEiChXPDD/AMhnxrJ6jq+nYlTufG1/tMls1/SI3TNXSGK6StTUgvxVpoqmXascMYPzcHyR4HfOrF2prV1S71V6ExqwYG9dECRkRk7QFBwD9R31LBFL1Aaunp7XaFpbfURr6ojyzO68sc9uSQB2yNV5elK4QU0YqvjaWOYpLCrrFUKW5U4z2Gsw13dRNRQPTGKtxeConmpoamehpfX2GWE8EeXKEc+Oe+qF0tNNUMbfFU1lREJFSOT18M2cYO7gYye/tpxfpT4amrP2galbgrqYlRlddh7lj2z2OpAaCzTVVPWUAusu8RQ1FWSqD3wg7+ME8afxZFRQF3hxkpbAuCZ+jajoqsNBcPUjuUaLJKElEiMGXILFeOxyRpvpWpauCOFHeobYYUqkiKhmK5BOfrxqrJLUVkNRQ2y4/CsShneZFVZUGF2bvzcDHA4xqnBR10ELRpNUzpBFMix+gSjZ5+Q9xz2J0s2dGbSTX1gqe7lus2u6tURQSVKRhBEkmNz+QfGdEktt2KKVhpQMDAaPJ/U++gFujviUMEVVQCljkYxt6y7pCe69hx986b1obrtXC15GO6qMH7aFlzqKC7wqY2I4mRS1E1JUt6as0L4JQ8Z5znGi6TwNSGQxoh35Ld2f9NVrhT1fw0ErRNBFWSFgWH5lAx8v29tfhVGlffahJXRbQDvix6ZAxnHudH1Er6YfVSneEvi0hpP+pBemjIVWQcZbnJ1M8vrtCyLG6Lkrzz986V0rBEskPrSMkj7iAd2D/b7amhp55Hip0d0QAgyZyQuubExWztAd7fmX2uMTVErKsKeiCNwIDZ99Ba+rSqkSaiXdHIQgbZy7+w+uiU9vpadxO+TT/lJjI5YDsdVJLczGnqqlJZYkJ+HSHBcMe2fYfXV8VI1iXVid4vRUq1lyFJM60swB3LIuBnPuNWLTapqaSrgWf1E9Uo0axsQh/wAbEDhdGRRzW3ZUrTrK4bJWQZJB850XrrnDLT1dPTLKtbMqCSdXMZ2AfMOPzA8fXTD5mJ01tCB0IomKdck1pkCVLnEsfyDOQhPf9dBK+OeoGEl9RcYG1sYGmKChnrohVXOVtsYJjWbJwfY47AgaswWkPVRk0TLBOASyx5484HjVkZU3J3iTZV4EUYDMSHrE9MqdrFzwn+xo/FbJqqOEU0QkMzoibOfUJbAI9yc6Nv0ZuDwxQzPHKd7c848aOdLdMS0t/tUjs7QQ1ULoBltrBx40Js6OeZysCQLmqfirTt+FNtsSCL4q0TwrA8ckWWgKqAW4/r9dL1phpusLPIIZyK6OMNHkDEigjaVYdyV4IP0763H8VLPXdbWu0WiioGrzFmWV0cMqJ5Oe6n/xOs06S6Gn6MrZrdcKZ4o3BkpyQP5Z8dtatnSBPU41ree6GqS02yOSuDw00UnzS7QRFwBl8Z+XOAW7DzgaE9T1UNsnpJ66mi3sXjiEpxFKNpJQOOEYjPDcEYIOtDq6EzTy7XjFFNGFLgBlOR5Xz3H3BIOsquUSChrLHLLEaaFlimoKyQjaRz+6mH5RyGXcDsbztJGuVrjjg6bEybqqiMl7alCySwOwZUUmOaPjGxwcgkDBBPce/nsz8AOhrT09Yoa6RFasqG3rI8KAvnk/NGxHB452kYGRrjjovp6fq/rWO2Ua1lwoKKT95KaZJMRK3y5zwvHBPYZ48a746WiNvpEp45pR+6VI1cDKjAHzEYH/AK0TPk0KEWZSLrYuZpUl2hjAjZwNwzxye3Pb/fOq01VPVQqUjX4dhuHqYw39/Gef11FbLUYgZK0KjHGecnjtyP6cZ5Or1SismwgBM/wgHjQOB6pFLdCB57pzIiMrmECWIqu/5T2yMdvf9dJ8/VU89ZspNgp5CDySMNkgtjyBznz206T2KGokkIWMK4IyONgKgY48cfzA0qXDomaG4rJa1aJPnCAfKueOQPfhB7ZzxoiFfMt9Jct3V0srQtI5/eGM8fNuYp2H8u/sDo9b7utQ8aumybhTgZI8n7HOf9jVW1WJaUrVn0y7xKcOOQ+MfbI8++dXI4oIxHGsKbCpZpDnJOBz/MnVHejtLBQYQklwhli3/wDw9gfJ9tULifiqYoN3onwQMD7+f5atU0wWUoykZOee544/39dT19Ks6GSIqSMHgYzjtk+dVDBxcqRpNTiz/iS6M+CNPdLfFLJHvO5vh5WCqB7JhVUeSxyc6VuhbxUrbFr6Cj+IpY0MFIZZlWWWb+JkiUHgdlHGOSSPHWnWXT9P1BRVFPVK06yKUmRxlOx7AqQSCdcg3Xpu/wD4b9Qy0bUKvbm5oZtyyB3JGE2jAHJzgAZwM9tHx5O4uk8ya0Nqj1a5q+vrvTuj0lHAArVApDn5Ryq57biRzjkgHk6NV1spblbq2SMKySkoFHGQp5GR45xn66z+43Sss0NPTTwvV3KtYMkNOhG7dnYmeyk7S7MRwoGn+1UUj0VvpxUhZERZZ2VgEXBztX7kcfTnQWBU7zUtWS7gHpr8IbteKGrv9fRiSoqJM7XwTMoIwiLnhQPlycDvrVvxIugquj6SutklRHJTKsZEEAm9DGFIMfYgA4OiHRaSRlmaoeOkUbfTjb5WOPB9h2+uSfOperLpT0tOKK37Akreu5WIKM57E+ftpbPlVMTE8ATO6k+hifYzAOnb5bq+iWio6mvtFwaUs3q0r/M4YgbuPlUjyPBGiV2paCgkjr56N6iVICtQI33BQR5+uT3+um6snN1gmoxKc7cfLGFKjOcBvGojRw1FI61VKkKyRkPEV2uTgjn315EdZhDBlX7TzgdAKq5mMl7qbfS0kUlSJmZQ8oigVQi7vkVmxyffPbTJQwU3Ul7t1XV0YELxsahIiv70qQARxkdz9dWbrbt1HbaiidKVFTmGaIFQOxIUd/11D0bSyQ1NW80SrPBI0AnVCFkjAGHz2HfB0cdYKOQjzxLrnCm9O0P3m3RU1suMFBRMYmwIzOclmHOfsNCojTV08ENWsCRUiLUPNOgHqydgu3yvtrzUdSvKlXDaqiKZYDhW3lwR3J54BPbUVtoaQ+v1PNS14mpow8SyMqqwyNyhOSffOi91KBP95J6gb6RUp1dNDT1AkFDBEKqYmEmRUwxYcBCCAD7atyXAW8uZXqqRFGZvTX1VC9sZA5++hU14qap6e4V9rNwpBM7Izj5Tubg49lHv51o9NcqOCIbKaNFZOcRjDAjsf56Bk6jEnpcneXTOKo7RTe6vdEppaCnb4WXBlkUZIAwMAHsxGNWxSREZFnqMfSU/66OQG10VKKejtsMMRbhUYqFPuNW99v8A/wDEn/5nTSfEenrz+UOmTp69RP6zCBZDUyTimSpSVSEVj+8y3khSflGoq/p6rt1qFto5kjqHcSyStHgHPfIHIH11sD2ShmRlFOxWUbiSdhJ8DPfGvsNhpo3jUU6BsY3Hk/zPcaCnWadzMnQx5MxOHpKcKsdtEcrOvzOmSCfqMain6Wv1PveGikkQqEDs/OSeTj2GuhobK8EWymhjjCj5fTGP01PFaJCx9eJfy7dpP+uiDr2OwEntkznuDo+6VQalmpyYYjgNJyGx3OdF6XoWqgiw8iJLkhVTx7HW3v0+wXbEyxg9ht16j6TC4Z5SXBJPOCQdBPVuZIxusxFejt7Qy3FJqiUDaFZs/KO3A1eh6FQqrRQtBUHn5z2H99a+nT1JACIYzvB4LMc8+M6lWwlQhWFY12n5s5z9NVPVOw3MntNUyVOkNwZGndFblhGnDfrqePo6OYqcFNi4LpkM3Pc861ensbuTiJEJ5BLf01N8IYpSnohScAkDGdB/qCPMsuOtiJmsHR6JIjxRnHhmYnOiENnalmikgdYyjBhtYKykffWgfBU7/mQOQexGpnstNBncq9g2NoI0Pusd53ZY7iOFinavs7VtPWNFctgDFZCrED3xzj66zPrGpnrKhXus7LUElIZ920hs8Y8A54+udG2uMdrpZ0ikMJThXThkPuB2I99Yb19f+oYFeOuqRUipRvTZ4AYJByCHHsVPY58Y17bDl7yg+89Nhc6Q8hu3Ut2t/UiWiOJ5lWNZU9Mn94pyCAM88g4XzyuQRoF1NUNdq6PMjVNWjLCPgo8+vj8rAtznGOP0xkaHUM9cCwu006zRtNFReqGcek+CYiCMtgqCp7jAOc5J0HoC5yVN7WUSwXHaFjkZI9rK+QAC2SQPGSFOc4zp8BUFmWbKzekHaNf4SfhdRdH1D3WrWonqZ2M8vxMPomNQT+79MDaxJPcknXQnTcSz1LV9UiLKy7YSX2nbntx4+4+2lGole12mGCpnLVUpMkjbSxZuGyD3PA8/10m3/wDFOljo5UoUr0YZVGWNxGJAOxbgecnHGkiXyPsLlgoVdzU6Bqeo6aKf4YMoO0EAjAbPYDPng6glvMcdwNICCD8odTwFIBXP6nv9Rrhu5f8AEexrlo6+NkokfEyvN8pO5QXJxwf4hgkZXHOttvvXcclkjrErM1sSxSKyJlhyxZiFzkbIiOOxK4znUZ1fHzIxIjH0zpKjIXmbGMcjHOcYPHtqWpZDE7sAATjI7+2fppO6G6llv9ipKyZ1MjQxiUL4cDaxH0OMjTFUMGRkbMkYJ3Y8Lk/+tWV/TF2xkPvLHpxiILgYJIbx3PP+/ppW6zvC2W2zSI6ROQFUs2OTkj9MLjRmOqd5UjlYKXwU445HbWE/jB1BUUJrA7SKXmzE6v8AKgC7QScHgM2eOwGdAysWXaNYcfq3hDrj8XYenLiLetRNGk6q8s4QlEQxIyKue5ZmI44znJ1Z6G/HKy3usqLfT13xYpkBeRA0oD45AI/MRnk9l7ffi/8AGD8T6iuhie2ztGy+lDKhwxR0Tvu+pz9tp9zrLfw/6kvFPfFioqp4oZ5TLOTLsUE8FiR205h6Yulk0YDLnVH01c/qVWXmjq9tZRSirzwSjEg89wB2P6aVup+l7f1ZC9vq2kSnqI2mpVyB6EwwT8rDscnsR31m/TvU1DZqanStvk1NcWK/uGjlkDKwB27CCSQc54B1qXTnUFu6ktkytUlyzhWxIWRh4ZOBgeNw86WdH6dwTxDjTlWhOcbvZmorncY5p5vXoAyw+pEVYJjsAx84Bzk8AaWofxHq7PVhXSQxMoTeW3zNg/MxGfzEkc+BhR21rP4yWuWqSmr7f69XVUEbGT0ipcwj8zBT3OOcduCPOdYbcLLTvQx3fp9v2qkjb5d6AGJvJZewx/8ArTupMg1HmABdNhN86M6wuNwpt06/D06IDFB+XKjyxJABzk45PudFKuWWumkkqJmnbvuLZOPYew1ivQtsiqKiJ1uFTUOGGfTkcJFnxjaT4751syRKjCOHZIzcuQxP99eb+J4y+IqRtFPiBvEABPGJI3MYcJnB79zqCuWXO4p6u3HAb+mr9NT1CbvUjVm5xlAR9OdXTVK+52pQZexzgL9Tj7a8kmFAbuYAC1vEyjkaht7zy/JFSO6vk5+XJwMeNVqWmapskNJc3kqZa93kFPCdoCEk8HVyqq5aaw3iJAIjV1kkYKLyQ2GP8hpbk6hpraLcBBVLVR05jRSSGVWP5s+M++tHtktS/wA/m8kVxAM1ruEN6S32xZPh44viJoZs7Qqntgd+NNdpvTXi7R0s1BUw00sLbEeIqodQCc58ar0tRLHJNWXMrDOzgJMHEiPEuNwJJBBB586M1V9pGnkrJFmecxKtMUQvsUn82fcjn7aZZ9C1Q2r+0kgEbQ7Q2iCmosKAjYJKhuFOrPwsE0e2GTK+QR50MtlxjraXNDVpPETsLtknPkHjjVytqxBHEY4ig2HdIOQW8DWK66yWIkAe8iNrpTxI5R1OAUydR/C2od6uXP2OoRUCVwd4jkxySc51W9RP8C//AOvUriWrFSpAj36C+ggXZl/lYdyCNe0UJvZ5EB2/KCAedVqWR1kI9UEdyyjOdWYkeq3YXCrn+Ec6Fu3EcVpWp4XbLSzogxgAc5OvZp2CKXL5HHHbUbslPIsZQ5J4wO2pVkn9UB8Ih7nGcDV1LTtVybD7MEllKjjJwNRGA53ZZmwOOdWFkWNVPpAIRnIbBbUbVqxll2KgPGM5I/XRCyqN5U2eTJU3LGwPcnkZ1IkzU4JOF8cnONVXmKD5pF3gdl7HXiOSRld4yJVQYbIyOdR3iTQEij7y41aoDO42gcsO2dQSV4Y5lIjUnIIOcjVdHkK73KKe20p41BNVySNt9NUQchlUc/c652IXedemFPioe/OQPmdBxqrPd8sWhEirwvzHjOhc5eRd6AKhwMbxwfrjU9LFMOZJEkAGckk6Cr2aJkdwz5cVp7nRymr/AH8hGG3xlwP5ayC92yhFVOlOJkTu8sDEIB7YOfr4P6a1G9VlVa6edAxPqISgPAz7cDWVrc0FFUPWvBFJzyzbSp854x57a+h9H2+yGXib3SsGxgCKl2t0u5KaiaOsr6tlip0QcrxjIbsG29yPHtrZfw16YpeljbbdH6EcqhpqmRC3zyb8hU2DgnGfJwASSMDSv0NEJKas6jq2kNJT/wDSUCMP+64UlpMe2eB/706/hdVpLW3W7STyFKKmQbKmcbQzHcdzeMnJI/TTDZS4NRwY9Jl/8TeuqS0VMVLVTVENKiszUictMQMnIyABgjOWBOTrl78RvxzPVVuaz2809pZXYp6TyFIsgfKHYZ/MMhjyMkZI40r/AI3dcC89Q3VKKpaZWn9BWJG3YMlioHbJYe/A+mlTpNT03dILjJaLTflTdvo7rAZoJUZGUghSCv5shlIIKggjWlgxriUajuZn5sr5WKoLAipNPUCVvXO4nJOeRz3I9s62r8Nerq+WGkgyWeKkNPH6abjyrpgnnnDDjzyfHOQVVKkNYVKgUbufTMeWEY74AJJ2jOOSTpg6buv7EqWpXd2hkUjMMnp7sqcZIweO/wB9FyqHWBwO2PJU/oF+CfWj3CK8UUvzS001PF+YfOPnV2GPKyBuBj5VJ1udVWYo3mDBBlhtBxuYHKtn6gj/APLXC/8Aw9dTR010v80YNJQSJT+j8+TEqllCkj82d2Cf8ROfOusrHfqeql2TSb6mND6yTIFwqLkZx5JQnI44Gc86znwlRtNEOGNmMH7bp1haaTLRRn5nIHyNg8/Xjj38jtrnD/iT6gjprRbZYVkWRLk0O+M/IFKldvuUYLn7ZxrWRdjDT18b7YhCzOpVyAys5Gd2MLx8o+mdYl+L8S9Q9OTRu6rXAmeN5MEsQx2D6j8w57DdjjV8WG+ZLtV1OQ+tkYywwzemaguoDpuPqAZXOT5PB/XxjXjp+oqOn5KUUU4oLlWzoorWgaQ0cIbDSbQCT5PCkkDjnGrtYWp7/RVU9MGMLKWjkGNzfmzjPbJJ/lohQ9P091mp62gqamOtVnFbJOC6sSeNuMBcLyByDzk8a0UB07TMcW8odQ25rRfeoX6f/EFOoobcEkhrmWeI3J3YB1jVs5IyxyTghe+SAdK/BH8WWtFwtalHE8krUxdZfTBdjuAb+E5wdpb6jOlW52KKngaOGvoSihV9QwMrkn/xPnxx30EoukJoLYa+zGeIJOpkukq7I9+cgBe4APc4IB7kaFkXuIRkFD6wiXicHG1zvDqSWEVVHcFepik2Yp5PSO+KQ8EOh7DuCOVOQexzrNLxY36bpD1FaIaWqtlwH/1GlFOFjRycerFnj5vK5HOk/wDD/wDEit6r6bitt+/fPbg0LLvIywzlCRyuUBwRnvxp76B6gkuktytFXTQ1lBUQAxhJGcqcbsHPLHaRhsYb751iEtiUg+JqLpdgRwYj9PXlbb1AtTRp6dFUMPlVAq5+2eNbXDXT1kQmjAZT5LDv/prm6526e09XtQ01QlRHJKBHtZhjJ4GTx/MZ10DZIJ6ezwpORE6rmRCn5zpD4nnUdP8AMzH+IPpASFPiaqGIrII0bncFO4Ae+P8APQ2srHenb0/leTwrZKnt/I69xyidjFFOwCBiA6bWIP8ADqh6qTzvKNwyQdrcHj+/OvFq1Nt9fymFcoTvPUXlqYB1p4gJ3+TgMVCj+x1DfEuIoqquWJ6sRhUeClh9SVUbALYHJ/y1ep/iq2718iZkV0RRGBnhCQRj3JOtit9A3QdjbZUK1/ry+xaaJXdAUGxDk5UfUjvrb6LE+fKdWygCz/PJNwqIXM5vtPS9XW3trXaYKy5x1uDskQhtp4yM/lJ5B+mtKpvw76ioYcfsL4WOJRvQzhHC47588DT70t1lXoLZVdQVPrXmSJnnf0YwzMCdkH0i87u+SRnQG7/ih1DUT1VIwkoKfZsSJJd7fK+SQ3n7/bWvkwdIw1ZHP0H7xkY8ar6jEejuaBJ4VoPQmiIGC+0uwGM4PJI8+deK2sqVg9JYNxI4weAx9/Y6frp+JtnrYgaLpyKSvamSOWqrYgzCYj52JzlifB0nGQLUM8cUe9nL+jGxKgEdvf31hdT0+PFRxtqi7lBwbgmG5wtAQ9M0NWCdy4yCPv551MKuTAzvH0A1YCQLKVMIVu7bzjJ8Y1GahiSTC4+wONAVUA9YgbjvTXEKSpeAAnPyjgDXqOoj+ZWknCk8bPGhbWSnkeEetKCrZCqeCdX/AExEcxM/GfkwckaWXURvvGdLSNWWSSX4czVG0nPHGpm+SIkxspccDPf7HUCMY2LSqx3DO3kZGpPhopBHMu5STkAMfk/TVQL2M6q8z2rfuoFMbbgOeckffUvw6lWcogHJ3M3fXjDxrvnjIkVs5Y4yP9NQyTZJXbFgglQnP6DViijnecSZIkc0ZPqIjDBKBmByPca8yRVETetBUJgkEr3/AKariCUkvTBVc4wSvOB/lqZCY1c7ySMEFRtP1GfbVe19pwBM+STvK5kaXMZPgcnXx5KGSSTYjsdi/LLkAe5GNfJg6vJg7d7eE27dVjIwVws6uCu1mQZYj21DWo3nHbmE1EaR7YsRl1Cqj8jHfjUUiFaf1AWUFGyFHdvA+2hdPOgYYikU7cHe2f5DxqJq2JKgspefkbQvt9dLO6MN5Jdakt3pI6i3EerJLKBlvTUqPtz51zr13TNS36no44m2VZ2qGPzOSTnAPYe5++uhpAslVJJG2xCPlVnzx51kXWFjEvVVNcriSMKEjOMttzy2PHHA16X4J1V43xHxuJofDn1ZCvvGu/XCLpz8M4qWBFhXYlPAef8AuORukGOSR83b+ms56hvNw6b6MFFRLHJXXA+rL6g2qnGMkHuzZznwBj7k77d4r5JT2unZmhSRCIsbtqA57+/v9zrUbp0fT1NHFWpSR0sFwQQQ7sD5FBy+45yCc5/9a3+neiAfeehzLsTORrD0TU3KllqqOGklVlBnW5OIoZG5LMXY4XPAAHPtk6igtEMFbBSraZvSmBMKtMWilAPghuQDkd/GtE696dp7MfWehaqttSuHdYgEUL5GC3bx3zqpbrdQzn41dtbVxRMqwxSktgLkDByRkd/fHca9EuSxvxMbt0duYAj6SqJzLV0MESS7WbbFGFBwMY2+w450u3Toe4WySnnqDHKtRvGNu4A57YyOxP8AXXQfTiwC1/G19MsEUkCKsUsRYouN5DZ4BJx2yBj9NLFdDD1Ze6WmpkLRSOGUvwsZz8w9hgDOcd+caucoM4YZ6/DH4ix25TC8qQzsyMXVGcFgVBWMjkDAz55HcHOugukK+ApsttcKmrO1TLE5w0m0Ajk9zgfL4bIPHOsPqmpKa7ItTTyXCsQYMdMwbIwVAx3B75A/TGn78PZqi4wrNDBBSUSSPLFBThR84/dtu/i3EKuX7YGR5OqVa2YYenaarzJTNFSL+dhNGQCnhVLHPHcNjz9dY91ofSrpzPK01PMxjeTapjBIGMDPzbTkdscEDWz4n+BQxmKSmSMRuJsH02UKDtGNzA/KTyPpxrPuu7TLMsk9qaATiMkhXxGVySylTy2Dj8uCAQADjmE9JktuJhXUXTSXG5LUxyrNJFKEcFSWUr3POOcYONOCfh4lNbZJaaHagbdhMhod2MMwbGFJPk48Zxq3LWU3UlC8crSUc6Ac54Z+4YMo+UjGMkdj/I7a7rJGIbdWQRLBVJ6cExl3GRR/GuQCRk9/Jwe2qnKUNSRh1biZncej6C6gV60lJIF+V6mmUM6Lgq8TRtxkHkH284OvFdBe61qexi/RV0FayxQxNDseEKCAGHn5T58Y50d6lnpunriyU8vo+uxSeWaUhhIMgOVC8Zwv9Qfr5/C1Km59V1ddJRzT7JAHlTHpgqPcfNjP6d9L5MurczgmnYT50h+GdX0q1ZU1UTx0syhXYgZjlVvlcEcg4zj9NFGWp6f6ilqZ98fqHeJEYgMGZhvTP1OeMA7uwOujrJaqImojki3TVUbI8BRmDKRz3/L9Dz4Os1ufSi3egrKB5HY0skkUW5gZCmcDt2OMZx7Z86xOoZgSfeP4QtAe0yLqaFa3qqjrYEaRajYKj0zhi2cFhngg9/Hn762or6sEEULZVFC524xxxnWRdPW+V77FT13yNTsQ5YE9uw+nbvrUzN6cJWmDKG+bhs5+mded+IZDSrMH4qQMoEvTUnySRUsa+rF//ZnOCfP66qQVECzxvI0cph2gY8NyW5/XVKKoRizIZEY8OkcnsOCff7apVdUlHTzyxAbiu1WI7O/yj+p0lg9aELzt+V7/ALTFBsbQr07arhfbvSrbl9Gb0GqDMW2LBmYH1GbwBkabLpW2zpietAudReOqriTA0qrmNcjJbg8nzpYheooES1NOPQmp8zKPzOV4UZ9s5ONFPw1tc1RfWurek9HaPkf1jhnmdflC4858njW90BBYKBbNufkPkOON45ioGhBlNdquC2wNFUU0HLM7yQ/mGc7X4zjPt76FVwqq2eGWOanWE8boAVVdx57nONbrL01QRWipufVFsN0mnb5KRGCNDEc7QNp745J841gu+3JXVMtsgWO3JUtFDBI+XRRnI3+Rn/LROrw5enxgsefz/KvaRmGhaO8kkSnNR6XzKQPnnUZTPHGfpr1T1CQSkGZY4i3yHHzYz3BH9tDKmqWlqKeCUimFShkHqS5AX/POvj3RJV+Hoo05YjeOScf015zJkyeYjXyhx3mIceo7orHJ9IZZfrr78VTjj1v/APg6BzXAUaRRVE0kUsyF8bx49/ONeFMjqGGCGGc6GC/mEFniaXmoIj3xLG7cx/OMbte0SSqO2qk9GQeQ3nwf/WoRsiaB3Kq4UsGJ/KPbRO20lNdK1LVJV00KTKDNV1M2Qo74ABxn9dHRGdwoPMZDEmovVFRGlQ8U0pZYf+84G4/Tj317pqykkllFJJVSY4DCFgp+2daxa7d+H1rn/wD5FBcK5Gy000o5OPYcf30xf84dKpJ8IKi2DOAUWIFf1OMa3E+GY6t8q/n/ALjPZU7lph8UNWQ/qU0jQuDksMtx99emoUooUqJKCeOnXK71cMwc+4zrT+s+pOmpLTVU1BT0z1rKRDLDHwHyMjcNY/GZ1mzLNK0pJYNjjP2PnSHVLi6VwgYN+0XyBE2G8uLNUFBHK8cDOuCxUkke+T24PbXyWCMVH/TTfEQKMOdhDY+x41+pMo0YqpmIWTdgDKn9PbUF1utLRtWyvMv7qMmMSQMyjtjheTpHGrZ2pa3rkgSenwN1OQY0IBPua5NStcXaWVlQyJEp+YtyQvjt514ggqh6j0yTOkfKMRt3H6fpqe316VsMNSRIqVHzqsqBNv8A4keceDq3PK2+P4h24zsCfKAPbS/UoMOVkLXXtxKdV046fM2LWG0mrHEGyR1LwCV3QlScoz5LH34GoJUaKZhICDgYRUxojJGG2sh2oVBwecnUcspdtsYLRN8pLrjjSRU3ZimkQWqepKykOxjGV2HnPtoJ1Za5K63LVFWjlX8uRkvxwPsNP1qoU+IiMkbNvbIXOcgfbtq5+IttiqKKlo6Sn2MVJ9NUx+p16T4Z0zIDkJ+02fheJhlDgzCuj+l3haOpqlaSqmky0Mal3kGc7AB5JA58a6HttE0VgSjqlK1FK+NjIXA4ztOT+UfT2OvPSlBP0t01TzWm1wy36tJBqI0Ek6p2AUnKoPtjU9iiagq6k9QR+k6xPJLG7nCHPHfjt+nOvS4U0H5megyvq+0yrry12yW1yQxREVzRkbUgLJnGR9dvf3A41zXVTTdPVEluuEvoB23emoIDE8lSO+CQOf5Y10910tVdZPWt1MtVTOSFGxyjAeAqgnJ7d8ceM6xKv/D2sro6msuhNuf5UaR3jBDOMIOCWHJwQcAYPvrQQnzFHHtzDFVd/wBowUFEJTxSx+mzfvWDjkZC8fMQWI/8R7ZMnT9SsVaIZI5YI4ctOdqOZE5yRuOAQ3zH3HA0HkC9L2aipKQNUb1MTu42qzhQZCOc8flOOwPfk6pteGihkhjkNPJghxGF4BYjYMeOffGj4VOQyXfQIX6suj1NKaK2VcjUkZLPKUwZDkDdu5I/Q9z403dEb0oY/UqHp6V4TH6aYjaX5eQ7AgsvHAJPb641k0tPXV8+wRuyzZ2oueRjGD4+pGe+n62D9jWswyOrVAGVRA2xPfIJzxjn+WtUYttMQ7ltc02t6xp7fGslFSNsDRtGN7MEU+MnJ7c+cD37ahe401zhj+KpYSPTQq0fyGLnhlb35POBnkayyo6ranlEa07uJiyqgnwuDxgewU8/bkau22/1k24vGiADa5JBHqbic588AH/7tUGInaXOQCDKu61PSd2hq4p09D1hujfOXBb5gwwAjjJPHBGPGdErzPStTtUQb2pqoD0hG6uhRjjG0YBHJxzwc86hq6yG/RVdNOyFirRupVWWTOff+Ln+Wk3FR03TJRNVCpoGAKuoCmE+V9wM8e3vpfNgJG0JjzaTRkv4iS3RqagZJPhEqGDJJKQo29iBjO5e/bPce2tJ/Biamt1vqJpRHHvkY+otQYww5BxwCeTnGMnjnvpJtvWdsq7MKDqOlE1JvdkjkYFYHb8uGGMZ5x39tOkFspa56ass9UA80YMrBNqINwVFIc5GR2wAeOPfWSwbTpqN0C2qdGUFaafp8TwvEyRqBtXO7YRxyfr4/wBNAKWzS3atmraY8zp8z9gtQvhjj5Q6gEN7gg6FdGdWw0sDWG4zxBJ4CI4/UOY8ZOQCNyEkZwedOthpZrY/xdnl9VtimrpySQ6YzuAPBHfPnjSTrZAaEBriYNCjP1Nd55Y2SWObDpt+XcO/btpwWngkT1UkWISEopPOG8YI1bvdopqXqG+1NFGyRzVIch2PDMoOPqOdV2V3hf1lV4PWx6ZGCqY7jH99ea6rHjOSnnnviJDZ7PtKHxUJYrNGhy6rK6AggdiePrqK8w0oWX9nyGWOneAkDnflxxk+x0Q+AprbNPLE4rIUyZNjkMABx9//AFoTVwRGAtCxQfFQncGyME99VwIqNQ8g/pv+0z1BBkNbJvFyMswR2pyif4lyp8+Dlh/LUfTdyqrJXUktuEkH7MgX029TIkIbgnPc5/vqKvBmpLl6O5jLkb8YI+YKDr3EfVuDhSrCnpl2r+VSQT3+3vplcoxISvg//IA/vJHE9VNRdWmqKyStqo4K1h8ZF63KoxOQfrk86q1kDrC9tTa8TM1SqbACmMDhvIPBxonXlKe2w1ZMccErmBmcjIcjsV8jUNNBLJHC0ssEkynamHwxXsSw+ug68+YWd5wN7SnW9NW6qpDUVY9GrSPcADtDgd1wTx74GvlJPTRrDDTUEypvEQmbCIRj82fPfQjqutp4LhQ01aj3NFJxT00TSEN23FRz2/rrxNPfLvBBJa6Kuo6QSAskoVA0ZHnPK9saE2B8h3UiW5HEY7ulFHE9NXS0tLG0JjWWSXABYYwD3z37a+wXGzQwRxrTIwRAoYFjnA79tIl/u1PcKGI7YfjqRiaeInbsl7DJ7N/przH1/XQxpHLYZWkQBWZaCYgkdyONOYsTAf8Ai+90P7gwuO62NTcpBufYYVd0buzdgNRCkjGNkdMfVYsxGTjXyU1KyenE1PNtba8pycqPuedfUEppj86SLk5G3AA8H7aw6DcRbUZPDHEhWGWKHe3Ib0xjURVYZJxgyArx8o4+2oBEkqRuXw7Z7Hj6jGvMjzQrHHNUQelJnKY5UeMnVDfEgm+ZZaXCqHBZO+Qwx/LUfxLNuk9WMRqxGc6pQrDOXkkkC+mAAduAV+2pKhqV4glPkrC35QOW+3vqNBIsmUqXxOhQyHvsJ3DkgZxoXVSRVU5RPnmqIvnR2wCB/wCtSyTxU9vjYpLFM4JlAICbfAx3zoalbaVZVqYHiqfmWmnKGRw57YH8IPv20xjwl6I4lypFXwZetjFKClFQwmCqFVy244B1fYwSj05aiXeshxvJAQHwPfS/TrXVIljqJJZIiAQI1C7QPHHjzq0UaCqM8rQyRvt+X1QcE9seBoTFQdt75g7hlj8IYXWVREcjavO/wND4bjPDJJIIgI2Oz02HZh/lr4wpknrHxGzlwjFX4X7HtolFURzy+nKVhjyPnQ/MeP5akLqIHE6/nGno6DdVSvIjtGfmARFZQfpxkau9QItVVsm5go+VmHc4/hGjPStJGKJ57dJM0bDGzAx/bnS1d6ipjrpVlUxnPZe5z4HsNexxIcXTqpnrOgx6E3jfZa+G0WeGX0m9Vvkj5Bz7d/Gs4/Ee/tDSVVUXRjKqp+7OQSGywLZGcccAfbU89zBoHi9b5o3Kk7sLH9AO5P8AvjSv1vbhX9D3arpmX1YY9yMVDMzbeEAPb341powcqIZl0gmZs/4gLPIErqqpZYlKICoyOBkDb3J87j2+o5GX240nUljq4ZauOnlSQFXiX0mJXPIXbvwB3HOTjgjXONw6wniuJWEMU3AkM5I3Y5ONaHYeq5PgDI77nqWAMq7iwIYcg8c9+fGBjGtlUXgRDu3K1Xda+eoNLWNHMnPphmJUkZw5YDsQSeB2BGrktwLUJhqJQ84JZgyemIye5+2MDz2+uqt4qfWmClN5Vm9TYxI5JIz+h57jQ56pp4Z4TM3qS95PzlsDgZ7AeBo+PGENiCbKWFTSekw9xdDTsah22xxCCMhto7kLnyxxn29tN8fSUBqTEUlnlIKufUJwc+ee41lvQ95qrfNHNHO0WaQoz+oQVVhyAVzz24HPYedaZ0z1jTS1mIH9Z3kEaMDhmUZyQDjvkY+pGtLE6BLI3ihDM3ykl36DqaeWOeBJJKbIWWGVATH9j48fftqxQ9INWNLw0vqBmBbKbewzjt48abh1LbpadmmeH0mwFeT5VIYZJ5993Y85XHg6gpOtLOsM6ST0ziED1SG2lBzvz44HPvka4ZlW9oXtE1vM1656QmsCJU2KlkanYrvcsGO77Z7Z/wBO+s+ul2FyplMygPJIEnRs7Vbsr8e/b6a1rrDrO3tSPSqVrJJYo3VYmONjYG4nA28MME4/KT3bjBzVK9bNGWw4yXDHIdVAYn6njOB76RyHexC8CjJbLGajqCmoq+SV6CM5eBZQokO5cAZIBxycHHbT71B1nDaKed6CFo1mAZkJ2MpAwrZXg/lHkgbsay+81foXCOqpmRlVso0fP6A86E9TVUr07rAZXMuX3KDzkYPnjzpB09VQoylVm1/hD+Ig6t64tUUkCU9UJpNzF2ywYHuxJzz4Pnzrr39riCKNwxWRE5KHHA9sf5Z1wX/w00Tx9f8AqSyU8bx0zMEZwWBz347fz103cL8qSgKd8hBKY42nyyj6++sjrCMTxvpyXWOMtVLeJLvPLIo4AjEx2FVA7H3P18aAwBlillWplkqA4ZgoHphMYwSdNnQ8ERsklTWRLUE8n1WAJGPfUV0Fun+K9O3rbphFhkp5DJ6qjHGw9z9c6wcvTvnpl5ivW9M2V/TFA1MIqZQz/DyudglbhQp9v9dCLnmlSSny0EsU0QnQZZG5yCp/kRov6lthMCXOgn9RImC0okaNjhsbeRydAusKm3x9NXGqt1BV0s1JtMUk1WHyykEqVHfH9ND6fpciuFatj777/wA/KIp0+RTuR9LFz5VSPLaqeIR1EUtdURRKCeSFbLcexPGiR6eDxerWVcdqpzEqFkBebb6hGCPv58DS10U1fdOnLFebtFIlbcbjvp6h5wq+kSdu0HgDA1F1bVyLevVpqh4/jQEp5Y5CF4bDZ99N5Uw4MKnMNR32+u5J+/j2gMi6AAZd/Eiojsk62+b0LpS1MKxhTJsZyPBI4DcZzpa/5ujuF3knpCfTFGtO6vGVw+Rkr7jOBnVnrcQw3RbdVBKpqOFXLrnCHvnH6jnXyTpm0UFbZ6SCpqfiK+lFVV1LRExorc4z4OfHjGmcPUJhxPjxCiTfvXyghvPlh/Eqgu8ddbbZXQ2e5UhxVzOuwuMnhWI78e+qENroYJKqruVzu1baWgG52Zl2vuzlcHJB196r6Us9TmSzNCxt85a4q8YxVKfzMAO+D/POhll60ap+NtFNHHKtOyrEQNkZQ/l5bxng67+oVl9A+R97+cq2omhxPlX/AMr0V3hS2pcxdnCtBb2p/X9bHO4fbOTr3J+Jl0jdke43JGUkFfhgMH207WnrKxUdOo+BYXemDpUtQ0/rbeMPIjjx8vbQR+spJHZ4BRekxJT1Lcd209s/XQDjxvvf6you/wANzXIKKoqFnqoKWWelhG6omztVW74H/rQf1nlSdIvU+HydvzDOmu8pWXOlhkudZDT25l/drCATJx3OO/HbVGijhqJGpLdEJmdNkZkZUIHcsR415s9KUOlft/yUBs0IGjesnp5YKcxUsG7eCTl8HHCn++rC2q41E4jiEcuz8rk4J+/uPrqvWVbQW5qitVoo6TejyQfMu4cjBPf+2rc89eelpa+K3PLRVFN6gKVvpOS2MBm7AfQabTpjlfTq4G/+Iw+705AniSjqYkEmYkCnHqR87SD7annlaup4nM4iz3woj3H3x/TSva7rNY7G0dbbZs0x9d0aUYIJ/KOSePr403WOthvlIs5mhkZ2LemxwIwOyHPnS3UdK2K/IlGUajW4gmoraaCllqKWhmq5EXhWjLSMQey5+ug1MLncTU1MNvgSSaY7XkGWVBjuAeMas1nUslZcKyey/EVXowBKhYkVfR28l8sR9s9teqO02y62yplka5XUEmaOlnqMRsScgBl5bzxoy4WxYrI2gruFoKWeZhLHBJ6Gz0nPqg728sMeNe6qzNJ+8mmpqNThec5wPYe3bUouXoW9IoaakpZTtSKmjRnYKCANq+PudEZ7SY64w10m2TKkuwQkK3JzzgEcDnSAwXvXMoVircFSEIpC/u8F2LbUyPOrdJE9V+RQkbbfmDgZJ9j51ea2WlbtUwWOeWauw3q+rTq8bE9gM8av2K0mrMccAqWkp3LyIsYWNWxyAT4PjxxqU6UO+rVYkBd5pHTNRPS2qEbFdlGGWNjkffH+mql3oPjZxU0jpn+IFyCPtxzq5Yaulhpo/VpZATzhj8wJ9iDow1UlMJHKFkblU43H9fGvcBQ2MCez6dqxgiY/eKQW+Xd6UrIoKspAJye3PYfbSF1J1qthtlTS3KnlqLfUAhkRPU28csfcfQf+tbze7eL9CVcKYVb5YgAEDfX/ABayXrWxBoZaarI9ErlhnJx7Ae57Z++qIdDCNsNYnDHXFBS0N8kq7HMlTQSsskRxjnHKkeNMVikgq6SKeGMJST1DII252MuDjnsSDnP66fOsfw1pae2V0dBG4kXdLHH+YByB8q4HH+uso6TuH7GulXZbqfRgrJQFk25MNShIQ59jkqceCPbXoMGVXFjxMTJiKPR8xvudaq1k8NMWMnYQqe/HHPGdBqS5mFqiQB6jYrKzFQAp8k89h9PbXq61Po1bOWETHjnA5HBGf1x+mhVAZJ6yMD9ws0hRXbdtGe/buf560yb4iW4Mn6jvzUpp4qHKxGnUMH4y2PzHGPJ/oNCT1pXR4WmmKEoVm3ylzITjJ54HKjHt99euqKU0pRndKjkp6kTEqCPA3AHH6d9LMdDPUSxqFCCV9qu52r9efbS29y9kcTVLR1/W1aqGq5TJuJMbcDLDGPqCcnxjA5OvNJ13IBJ8XUzrmYlljYgOMHJJB7k4z9gc6zuljejlqI/USRYmIV0PDD6Z5weCNSCRpY2y3AdipI75PbQ94dcm0L3vrerrKyRo6ljCQMEgjcMDJIznnA4Oe2qlqvtSt6pKuWQzusysdx/Pu4I/UHGgq2upm3sgDgAMxLAHkgdj35PjRnp6EUVUKlhHUGCOQjK5Abtx+h9u41xgSSTDN5SemYLIm2RRvA7Pj3I74+uqHUkzVMFLHIuXfhtjEAkecY851JcKj4eTZGzyQxyFB3UHscjPKnk+f56O9FWKHqO9RtXt6UEXyruHDOTkgN2zjsNRkcIuoyyqWNCM/wCD2LJKzUsDqWcJVVJXOw+FUj/fB1vNipqm5XFZNqmM4Afnj6jS7050rDSVKU1rVYIJQUZQMozZzyD7862rp6xJb6cemqpU4H7ps7JPsewb6+fOvMdQ/wDUPYm7hx9pd4zU0yWyxmKaRgu3GQmcfy1jfVt4p1uPxIqY6WWJ0AlSNg5Tt9h761fqCuipLDPNBUSRSomcRgKyke4POf5659vHV9wkdWkY1MasSy1UKud2OPfjVseEOLJ/m0T6pWZgVNRonWuvE0dfI9XcNriGKrkGXlA45xx9dUuo7fHSUDRTJJiP5plQbTMC3zY+v11RrusHSdKXqCkioXhhRo5KFWjMZIyGBU4Ix9NS2zqey18L0cNVm5VbEb7mpJY91VXHB5GdDzdIxPcSiduPlE8wLDUw3+UtWbpm3QWf4KtrpltdTiahSonxJFLnAK453Y7jGNfrtYBaovjnrJK000ayJA6gM2OMj2++vPXlnqbZR08luaCvu0iswUxncVx/CPByOMaEUgoqmKNVeruFWcGcTSCNVkK8qVz49u2kMuDt7ZQL/wAzNyJoFk8+Iw1TUlA9bLBSTTQVlLGYZ5AG3EDLnJ9ieRoJcIKu526CpsMxqooIgKugjkCTd+HiHkYzwdCaW7XK5NNT0dEhplfJnd/kRexVVz5x3052u0Wu3paDE/oR0rMzxqQVb+Ilj3+2fGh4qUFgf2lEy4tNMIHt1oulJRTVskAf05TC0aTenOquDtYj2wMMffX2o6KmZ4p6r1IIIV2SDcuxQcHBI/N45+urlrrhNf8AYpqZGqTJIrH8sn/hn2+mmwSV0fquooaihXiVGfY8YxyD/Ccahcdm5a1dRQqJclHDZaoxU1d8KqkGaOFAQ7Hu2fHGmD9pUniVnHhvh+/176Xq2lrrtfUqLE1RQUVUhDCqpyAWB5Uf4lYZxzpcmo6pJZFFROoDEYCHjn76igvmSBiH4uZtVxsNbTQVF0pYnmpc/PHITlRnBKIvONVY7hDQ1lMhov3lcm5SXCADsck/z51dm6jvHTs6zXGym52yRQZ5KeQOSD3/AK+AdDLu69VVbXiyW6sit0abZGeBSWP/AIITjAGs4d0MrLub3Hy94r+Hg1LApVtlXJPDS0FVQbQ0IWoErIMnKnwRnVSputDdqKGC/UCzwh2FFFQysFUjljsHBYf01Fd5LVXWiS327NVW7RvkaJoh6Y5wI14BPI740tUFdb4epKU1qV1JHNThKQ29kSNWAxhywyD9ec+damPNhzuExuQANxXP395XSaOrmXqCs6aguD/s+nuN3Z2crHVucF/YKCDxjudDK6zr0/fWkt1PGYq0mqmpZDvkQ8AYdvl7/wAPfQqyU1yStlqaKnpmpoqqRTJTN6s5dTwW9s9uNWK+S81NztxkhlqwrH4kmQEQyMchmAxjH6aKrEvTMAN+ef8AUsgoe8p3akoayGe40noyyRlo3CzNH8pXs7jhueyjgaZejZbBCXis6XhZqWgaOaNN0scc7cZ3dtuBkFcd9WKX8O6E2w37qa5VUj08pFNSUTx0yygcbWViQ2T5Bzrz07U0tss9w+PrzSVlS8cJCyBxyTj5RwT450xnXuIUX9qlLAFxhSsltFRRJbrQI6i4U4pZliXc8jbeHBz8pz5JGraQmJfSvNPJUXSGMM8IC5c5wE3BsNnHYnQwVEdrqKb07tPXVMGZEmY7XU4OFcDAwc/00StryU9BcJqyuG+dFUr+dWychlbxg6zB07h1x1z+klB3G0jmEVnqaVCw209ZU5M6EgDtgJ7cDjOvkVXBSiFQGZW+VhDN6ZJ+oz41TtrUiqTU0y1EpbCsznB47HUUoj+IBWJgBnCoAw/nr1ePCiKFA4nplRUAAE06xosNuWVjGiHk7kOP5550fpYllkWUwrOhH3X/AF0v9G1KrQBpRISB2Jwo/of6aNvN8SxaYyHaOGWQ4P3I51R6DRxPww2kJMZeZYYIW4VCnzN9PoNIvVNthZJ3jZAWIzjByx4HGDx/ppqt8sKEVNaqM3ZBk8D7tnnUF4uFFLFmGmLJvwpQHk47j3++hZVBW5bGSrTn6/WOCiEKRljDLLkyKC3ABbngc5x+uuTPxi6NNjvSVlOj/CVWWyRwhGOP1137cqdXL+qy7RwFPf6/fWJfin0RDfbbUwyBDK0eYJXb8p7ZP89d02XtsPaW6jH3E+c5Oqa/9pJD6xMk4hWSNSMhxt5HPcg9++cZ0Hp5Zo5Id4MiIwMYHhie/f6d9NItqUdnu9susstJeLTMayiQruR4e0o++Srg+ykarJaqTqmnMttY015Ug1EDsBDKP8aH+HJ7r2GfrrfDUNuJishP1n2GoNcKg1MI+ALGMSRrlSc+584Hc++g1fQyW6EKql43bckme3sCT5H9tEaalqIopKWcvEA+XXP5Qv8Af2zqtJbas0yKpf0n3OFZsAf+XPGTqxa5UAjxKG+mk9MyhIeyhV7nHvjUbpAikTyhC7nGOeCc6mktRRaqSV6dJIiqbHkH5mAK4A7+c+NWrfZKS4LIIq+JZdyFFcbWJY8jnjAGTnVDUuPpKC08lxmCxSKIQAoc4AA7n+x/loxRwemsiwy+jFTxsu6MgtvYY5H+E5HOeNW6fpKaWEDeZEEYlCRv8rJnkDznnP66KVVDT2ymaGtlekjjwkiFQJWjYbuPfjwfI41GoSCp5qJc009ZVt6+9zEqxhD7KMKuNdKfhd0sLd0vQx1sCymsT12IyQxOcqw8MPBHt7jWJ9FWc9WdSU0NogVaKF0SUSuQXjBBzIw7ZxgfXXYfSdlRqUwoCqLJmNW7qe/P2ORrL67JsEEd6THuWMK9LdPJS/upXZmIDwyj5i2P4Wb3Hv5HPvrS44qenovUVHcbMk98j6qe+vFhoKREh2lo5shwSvAI+nbHcaJXExNH8PMj0/qA7SgBRge+Psc+2kESluOs9moh9UXWCjtcskSSMSBvVBu3f/ae/sRntrHa+rvF7qoKU2W2UdvqJNoqXi9CdF92jzz+nOtc62sNQtEKWgnaRZhtBVtrIT2JB/uDrniO0/iD0/d/XuSM1BDUGJYpwZJZiwwHTaMj7HTPTsFWr3iHU5Qr1GOt6LrhX/AftE1atETTTxxswiYc7JB4U+/jX6yfh9crXcEul3Wmp4KRw0aTReq0smMhhGD2+ujUVpv72u5VlQZqqIfu1Vh6cyjHzBgO4zxqCOZoKMRyOgqjiNA8mXDYHA+uNKZeqTCSR/yZ7dVY2jU9F0j1TWvU1jT1tRsG2ESPEExwdq8Y76s13RdshVouno2oq6RVMtQ2Z9sfkHd5x2PfQeCnjWlinp6p/Wnf0lY8MQOCPvnPP00Nk/Z1fT1c13uFeQJFjNRSyMhk2+Sp+U45z20gMy5D6hZ/aZLq7N7y3ff2X0xZ7jJb9jzQKIXiZgOD2fH0ydWPw0ithsm8SCX4+WWdDLwfTQkKpJ84BP2Gl2+WeKC2C5xNS361XCN4lqwGVicdn/wsuAMHQK81Fxulb05FakgpZnlZhEZflVGjMSc+2Mn9TpZygYKRUgKJ7vPWiVPVNdR00xls8c//AEqJ+63KrDJBA5zk4OdRteLH0fLW187VdbWVx3tblY+mihjjcM4Y4wc99T1P4YXGlt7rPelrq2A+qqQwssa4H/bBUcjjzqdKOgqqa0SXcUztTUfNDSAJI8zuWLsxzkAbVwPrpvVjS9qEcFACWP8AmC9fiB0s09vuNElXQ1BZrfEzRMqDBXbuHYj+RGgB6hjBxJbrwHH5huBwfP8ADpl6bjusbVC1FuoJLc0znc8arLFwf3aEYyewOdXo5oxGuKG1oMD5TMuR9Pz6TyMVyEEWJZkpt49Ut4+Gj2zVK1k1U5UmR1CIBwAFHcA6iuNkratIJKS4rBPuARIpDtHv8ntpNhulkhhraiSsoaGkppVECxuPk+jgfxEjgDxo3RdXUd8DvYK2hWJcKTCu+oZiRkr5Htnto2TG2JAh+lzPo3dSV7NX1Ulxolts7tBtjlljlEIZsZCowyScaGDp6u+GirjYJZ6YOq1AqqhGzg8sGHOR9P10w9Q3OfpmX9pw18qB2yInG5Mnkg/U+50GpupHqa+Wf1RVUzKrsoIWNF7lVA4JP086yOwuPIGXXYPk2P8AkKLqBrn1S1u6rSj6Qoa1rHUR7m2xpGsR8sGAyx3Y7/XS3VU97kuDVFaz3OnqKkCOEgbpJT5ZB/CB31ptBTW67wVdRbLrb6SiFQaZqSqm9KpLcH5UPO0eSeNU6WSz1kxt1LSNJPTVDJLVhC2w/wCLIPI8ca2lGTIy2KB9/P0hcWDI7BeLg+l6XgFor406eprvepBtpElyIVfGeEHcj31pvRX4G1fUNntNReaOXp6Vl3PH8SJXTbnjYfB74zkaZ7B1X0J0NaIFqgZr2Ed5pphtLNnBOSThfbRWT8ebLQ2Oe4rQtTxwyCKSqMqtCHY8AP7/AE8a1h0mKwzE7e00R8PA23P1mb9Wf8NNXaen7tcLTdRdaqmU1AhendJMDkqMEjOPpzpWsEFdS0SNVUAWoqyskitgEJgbfHHv2861Pqb8bbzbrLPV2awXS9SOuMUEsUzKGXghON6j6HP01ghr/wASLjcUhSnqreJx8j1iLEhU888Ej7Y1dMeJX7iL8oVOlx9O+s7VHx56mKqInpFC5GYYhkN9jovGtXUxrSbJadCcruUKB9P9nSPFZ+vhcIkul/tVFRO/7yYVH5Fx3GVGOdEbfSXH4z1K3rKhvP71R6VOQyj6PjtkecaK2YIaMKvUYWNBrmrUVwNutyQxzRSMPl3RzDK/zGisFc1RGpLPwv50fx9cDnQOD9mQ0Pq5kkQHO0E4B9hk6IUqmq2zQzSUYI5V8MDqMoNg3H8Z2qXoTBNKuYZag/U7UX9fOiU1XS+iysiwxxLwe50Ja1yvN6lNUCJOMgc7vqT/AJDXmsbaUpKEo0rZ3O3zEe5xrgdpxG8WqyCa9VjRRQOypyzr8uz2yff6aD3e3I0ElLU5fIwGYg/fTrUVkdDSfDKpZPIReWJ9/cn/AHjSndU9RsrBhvIMm7H09s++hFAOIVXJ2M5i/GD8OK4zy3iy+pJP6TwzJkp60J5KHH2GPr31gclTT0N3q46NHpKR5GQK0gk2dxjP2OD59x413TeFSVHhFJI7MeDkKP5nnXNf4ndAJNdIUo6qmoFep+dCgaJZJMAlmH5dxxz74z760OlzafQ3ES6jESNSwt049sv3SSLbqRY5rfRR0NTvjAZ6hSz7ifIOQQfbGs36m6cenDO7MGJww/wn2/trd/wb6XioPwxgqrmvpz1FRO8ibMSsUkMaKw99sfftjHOlPrOh/ac+2CnMKhiTjncfc/XWqMahLkKjOBtOeqyiMMaSFTgON2jVmtscgMzrlVPb30/1fSg+EkDxbg6fvCRwB/8ArQvpixzwUmyqj3wqSqP4YeD99CqSMRVt4zdNpHSKGVcbh+Y+D5Gkb8WrlPX9STevTrTx0cMcEbnvKu3cG+3zH9NPUCrTbVaNwCcbV5Of10brfwsHXVDBWSwGKqt7bmZOHmpxkvH9GHdW5wc8EHQyADqPiMZMD5cZKDiDvwBsjxtNPLS1Qlkpd0UvpsYBCz9txAG9iCRtLDC87SOemrChppiA8aljuILA5J//AFpV6bo6G30EFJaqaOKihjxDApIRVJzwv3OT9dNtBbhIivTO8THyo3Kf0z/lrz2VjmyF6lsYGNdM022RepTq0chSSMZATkEfUe2ql2rJZyDTtGYjywxwGHGf99tV7JmjpdxmKlRkMvBT6j6aETLVSGqqy6gklm29iPqP89Xb0pUqN2gK+Xl6S5wRJBlwCzEtkfTGqlX1lTVFsir6J2WokcRusQ3oreefbSbW9eVdN1NJS2uvihRpFDJM4EZ5xwcHn6HRJer6Slo5IbpS0KRu/qTinX0SjqSckgYHHOfOsnVkXJ5+0831is+ZmPE/NenkupmmnCJM4DAOAyZ/wodfa25xQUiR3qg/aasCDMiBJUP+JHA5YceNea+3W2e5vVRyTJUo8M0SuwOMo2MN5B576ijuH7PuMVFWipljO2pUpHjecjIB78ew76AiNqOv+fnFlG9QvdumoXW2xpNUvSRSR5cShJIz+bcwxgZbzoO9tjqDcKGaJIGlqlmWQHcUDD5lyOG5Hj31ZuFPcJrxDfaNoJ6R5mSqkDkLUU5/hdD+Uj/IHVfqS2x2ycVcVTLFHOGEVTGuViXGVB9ueD9NBI0GgK/3/PtOdSvBnqC+SQxV1uqo46SFl9AmmXJSc8K30GPOlOrsdU08EdDTw/FwxIrSsygrtAztx39/11auNHVWu0pErCaqrZ5auqbO7aCuBz7AA4++g9fckr6a23G3oPhDMyuwb02MqnBBb6gf01JsnSBwBv8AWVUWYxUFhM8sNRHdamnCSbammgcqZWxne4z8vscd9fq2gorXe2gnc1ddvX4RNpaPacfLgdjuPf7a82W9rf5GaOjakq5CWWPawSdQSCC5GN3B++jlZcjZLnK9rRWEZ9RUE4WRmYKflz/PH00xjxEk6zLjIVNRRkuFPb+pp7MnrPPK7xwlHASGZhkszHuQRqf/AJVB/wC5Qb3/AImGOT799fKTpOkpK43281KV6NOTPTrJiWjd8neE5D98nRf9oj+C5tt8ZRhxqnUYSCOxXzv/ALNIKg4IgW0UdrpbrSUElpFNNzLUGeiOxWbjJbGCc+2Tp9u3T9v6fkmrbStNRVM1KrR1ICxByB+X5iMgk4I0+1VTbrra6Wpr03zmlE8dIzASfl7H2PjOsP8AxW65tknT6W2GyVtP1DFtlpIrhKjRxLnDOuDhs4wM8aGy58+VAtAfnt+kQSi1cwPeOtLpPUvPIbXWU0CbZIECSQk553E+fH01FU9RxX+qoLbYXoOn09UTPHTZkJPt2AB84HGkm3XmV2DXKpgpvSX09ogVgx84AH9T305Wu/QxV9Ij2KRbfb3lr5JZ4DE8gVexPcgkrjOtorSFBz/KmyuFBjoeBL1PZY62onpbq0hPqu0ddLGQ0mT+XK5Lfc6ZrZVw9L2igit2KWoWdvivTctJUKcjjPC4440m3q+3W4JQ1XTdTTWypqfVlqVcl2UE/L6YCkDg4+upYrldaJ6WGvhanq3VZYpyirIGBxtYHOFbvnGj48IUBnILVzHMQGkbWR5qGZempb3eIbj+0o660xHAtjL6e6YA4+bdyc/potGeoq00PTl9p6OitPqPUR05QzGWQdtwH1P+mvVrtt3udv8AiK2eksdDSq0lwrK0+nA0ed3ynIZnPPyr9NF7dcbB1h1BInTs8tXabZDEGq5G+HZpR2ARcYBz3+mpXLrbRGuBZmgWW0PYKeaWuqIIp5vm2JhEhHsoJHPvqGoqIZyVgucUU7N/BCKjePY4PGohQ2xI0BgFU5JyYkaUg58s2jNPSkLGaChCZxzMwH6YGngKFRBwMlhvMDz2wXKjqKOUMsTpseQUwi3g9woJJ1VtHR1vo0jobdbQqu4Z0VQSy/4s98/7OnEU5poGluEoiABPyRDdj2ye+qPT1Qtf1HJUJOQEUKiMnzqvklsf050J9FgNuYHHix4z6RvPF4ppLZTemk5miVSFJZQUHsWxk/rz9T31XsV6jNMA+YlTkNkfN/Lzpm6xti3GEu8P70LhljfgqO2R5/vrObUtPWVHw8rQxhDgxxt6R/1Or5U2sRzG0cnu0Thpe6jkc9x7k6nt12g9AmlUrJL/ANyQLghfYeT/AJ6+UsVIq+lHCsXkySH1GOPvnGh092iSYpRgoufkcjBb3bP9v6aWXaFbcbS1WtHK+wqyKnZc7mz5Y/X6aX7uqU1FLKyOhAzGucnJ7Z9z9NFaGrjkp2en+ZQ+AfLN5/T6+SdArvM9bcKGmXCpvaVsey4/nyw1YyFipX0IpLfNPdKuoR3zmON8En/DnudInUnTcNbbJInjjpKeRCPSI+aT3Lf++daB1ZmW8WezUigyTP61Qc52RLyc/c41V6iq4aSBpI41kY/Iu44yzHH+eNV4MvzBv4WSvJ+EvTorKgVMpE8U0hADF0lYYP1Ax31FdOnI0nYyxgsfmUAd8nvpctN1l6a6o/YVUtOLbdHNRRiNdvp1BGZAVAxtYAHJ8j661KV4q6lgSTYs0Y2qfceOfvrcxOHUTZ6bS2ABfoZnFZ03BLSS5UBTweO/OhtJ0oogCsNpjwe3HIyPuMa0eSkZUCSIVPcDH5j4/qdeIKEO4VUByoUeNwzx+umKEocO8zKm6Uaa4YCfxcexJOtVskcNhiL1REcKLtYgcnPGpKG3U9LiqlClQpKgcHvoF1N1Igq6G1K67pj6zofEYOMn2ycAaXzMMaloxthxEniWbJ8tUkVO5WbBYQtjEgB52nw399aDb4VMQnpVSRD+YfldDrMljmib1oWLtTv6oHlk8kH3H9dP9vqgfSqKc4WRRuweJEP+YOsBZ50y7UXRqRnikG5TyvOCB5H6f5ar19c8ChfTL5X92R3OfGgs1a090ZZMYydvs33/AN99frjUJUUzxZKYXKENzx/v+ml3Yk0Jb8K3MS6g6NrIqlLpVR1MVBU16BJim30W3cJgnkd+dfelmq7l1Dc6uWUyW8mQCELn1NwxhvIGOx1pl9q3vFllzAtbNTsJH9N2+bb/ANtgnbjnPjSd0xf2rLjtSg9KRxIjVqU6oMqOVAB/NwMk6TGRirGt5hsNSMPJhK1iat6dFTG8olhjio5PUUqzRI5Cd/IBIJHtpmo45a62WqeueRAC8atJEPQwDnJPdW9m7e+ql86ja5fAi3PJEGH78gELu7YxjwdT0Fxg2VED0qVKpTs0zoMHg8KV7HQGzDLlYDxQv6zOUbkVKddLDSJGtu+Ilkr2jWSOOQM0KhsNIoB9vb30WrL1FXXW7dMRyLUzxSZpTK4ZKiMKAY9w7Ng8H7aUqajqauRKyhp47YZivwr7NpVM9h4BJyT/AC1al6ZgqKlUWkNBcqebfBWrIfS5OTnHjIJI8Z41TLTGgdzxL5FUbAwlWWc3Cy1kFDIBXLDsCsMlkXhRg9j4OgUtigX0LTdqZUqwFnlpcjKPj83HHPfRKsvbXum/atGgp5xKVqaeIn9zKpz/APi2O+nnp+jo7/1LYawq2KeD4sEn920SqTg+5B1XHkByBRyf7+YJVJBEy2naqMdwqKeRVW3wF0RTvUhGBkP0+U5xjvqt1fe6tLb0/c7dCIKC8UQlM8sZdY5EkaNlZQvH5c5+upupumbia6apjgelS4+swkWVwKpXB5TH8Rzxj6aB1NHdabpyyWEVtZcKm03SojqaGqHruYJURlVz7qd3Y6d6YByyn7QuPECQP4ZDB1pUXRpILdS0lTUFPTUpEDvHYvknPHfGvQvV6iAjxQfJ8vKnPGo26Sp/Sq3tlJVdOVySGAQLUZSYDkjnIjYk9s6+GmpgSJaO7+oPzZoVJz51c4C5uH7CHkSlJ1x1JbrxVVVhrkro7jiKSWoUvG7/AMKx5IHBOOONA6i5R328IvWNW81XJUxpNTpN6YEYyGQMRge48fXUNXVWaWtjrK3dVyUuAqy1ICR7TwqqnOPHjQy59VyXe81lWiRq08YEieiCQy8AD2XWtiwKoAA8Vc0wiY+Bv7xru0dF0tWT0kNRTnMyNC/ykrD/AAhsg8/XTLaqevuv7hmaqqJYWT4gTbv3bcnOOAVwOdLUNuvd16TsF2hoI4enliaFZWjRwZUPzNMRyMntnVSCrvNxt5nrqtaihDsPTWpCKyY/hRSMjPvqrYFVKZoZG0pTTTqujqaW3Q/9C9TUQKoefIACjtjb+Y55JJxolY6jpfqSSnS40SPc4iYpBJVtEJD9CPcHPJ0LsvTMqrZbWlFNSxXJPXmlrKoIrwA8tuzj5SMAedGOtOlrBQS0kFmqrUsry7IXWfblzzulCg5GPOk8mJWXQt/b/suza8VJsPcQb190rR09RVQdZ9RWxrRWxvJRUCxmeod1wFSMA7ix4G88a1LoWyxdL9NUFLUxSVdwKB5y8al1z+VC2Odo451nf4RdMCpvFfc6oJVfBP6UjVECszSDBASQ87B3GMa2xWm/i2pu5YqvB0/0uM48YVjdQSlggUniVf8Aq3ASlgihXJbOzcF/mdeneopYBurfS399mFH89TuDJ+7j9SYEHKgHIH31CtIiPEPRkcjjdI3H9dNSN5WmnoRHNLU7qtlQ5ILScY1F0Kk9JWXGUsQskwZUcYG3A8f7xjV+9zTWuxVVQwpaaEDlpZt24Z5Hb2166FnoJ6d6iJpKqmmHzTKBs3ewPt7HSjgHMD7CWUb3CN7rpK6KSNqhYiMjDR7iMj6Y/wA9ZFNJLb7pMadA8b/mlLmPOOMDnA5+mtvuVbboYzFRwQySNklpHBb+ePbWV9RW6a4TSSU8AWT8kZB2qoHJPPjH89ME2KhVE9DqGo9LEyNl8INvOP8AX76+1tyC07bMLGRl2HJ+2fc9uNLTVWDEqS+pULw2XyB/cZ1fprfJWTK0zAxhhvAbPPtn39/5aRZSGjIIqGOlLoy022oT03diUB5wD2/pk6geqM17ra0fLHFEsMS44VB8zH9TjUFzq0glLxDCR7U49znI/l/fQ+K7xt8ejYBkX5SB3zjU34kAeZ6tQ3XC7XecmWol208ef4VVct/Mn+g0rdX1Zhu1hpZv+zJWghe5LKMj+vOjVBWrQxSNKMq9TJJg8nk4H9ANL1bC16/EazRSr/01FTzTdv4iNqn786spucRUGdY2Fpdl8h3PVW6IyoivkgfxFR2zgk5PsNXeh+vaPqihAWZRcqEBKqFxhmA4EmP8LDnIyAeNG77SelSt62ZQsILKWIV17beME/bzrn/qXpG6xV8d66QqXiuVMztUPTkQtG5PCxKONm3jb5/rrQ6dwVq9xIw9S3R5ddWp5nWMNXBWOCzBDtBUN5OeR9uBqSjFPTU8g3RthpApPjk4/vrjiH8dOtbG5o7tSUFbUwHDGeJ4ZlOOQwVhg/pr5W/8SXWM9K8EFNabfkkiWOF2ZftvYj+mnw7e01T8R6Kr1H6VOlus+srZ0dZ5qq5zIkbEmKIEbpJDztQec/yGsr6Frqnqye4XmtRhcKg7Io2YlUQd1Q9sD5cgjOTntrMbB0h1X+JtxhunU9XUrRHgV9aDsYdykY9yO3GD763Cx0dBZaunobPI6JSw7kZ+JJRnD7/8TZ5B/TWf1LDSQTvMvN1jdU4CilH6x7tcqyRU8hUpvBBB/hbyP9NHHL2+lCoQ0DcoP8BPj7aX7e5USxTICO+B2yOx/wB++rdyuC+nFGz7o2wcj9P/AFrL4EHyYQqVh+BieZSs0uTvBxtb2++qIhElCJKxBVxmRVkjkbaGXPk/741Rq6qV3hgjcSbz8ysM5Hg/fTyKOlFrgjqPk+Ubt65bP27HQ1UsSROyfgqJFqp6uou1ZJWGC0RUg2j0MuixjIVd/bsfy4I+uqNTU2e1NJWWCigEsoPrjYrSPjndluFBx4Gfrq91Z1ELZaJCJpIIVlCFmf8AdBR3CqnzE/T66H/DS2eGiqWpqmq6jusUlRTUZUv8PTn/APulU+T2Vf11nZUdMh0LPOOtEm5EvVlS1EtaqGnjdC2GALAH6kY/pqnJ1bcEnjaaqSKm37pWjiVx6R7ADGSxOvFfU0afHJXPWwvTRoIfVhJWaXs6MTx7nHgDSHbOpaSS4TCX06dacnbI2Q3sSM8AeNJJjyKTzXP0itAihNurz/zLbJaWKCd1RAwYqYyMDOAcZGh9uqLjBRVEBWTBjaEtM4PqZXtuHkD+L9Dqjbb/ANO/CtDA5e5LCDIsUrD1AT3HcFvqNFLg1uqIIZKhaqjgp3V451lyF/xbwB29+NGIFAt+sqRRiUlTUWi4ieZJIPWiEFYocMZB4kBHG4e2tM/D6pmpKHqmlU09fJSW5nop4dyLioyDGC3IwRn2/TSH1dQ19vmlqLXsuAjwKmlxkSp5dfKuP66+0fUVXabMP2TMSiVMNZICMs8K7g0ZHt85P3GlFyaMwob8/cf9/lScbeDGCxdS3GTp62U9PU+qIC9NNSS/vMEAgNHjkYx3GlSrgqrdHc0qqCvpKu4VG+tqHMZmaQAY2IR2xg7vrovAaUXgS0IgNquJEyAuVaJn4faB48n2ydWWhNxulxtMUNbuWm/cTbS6ROM/KSBkDAwMn21uYiKDLweZqYArKABcQ/Rr6Weot8jzpFXTLK0UwHrFTwrBh215NbtOFXqNVHABrlOB/wDjopSQzzVMVPcaj4iuoyWzFzPIByACDyw+vOqbdTV5YkruJPdlGT9Tx302cgx+Lv2hMjBDR4mKVdJJNO1RURekxYs+OcN9u+iMSLNTo8a+gpf5pfSYlyP99tMdF01WXqW51EbwpPSgy1clZUKhKk4wAe7Z9udVKFQ1ZsjaWZVbauIyELE44z3++te/0htNR46K61uto6Yno3qqups6tshSpjUKshzvAA5K8jgk6vdN3KaOKa2WWjo6I1iKtRFNMqQKoOQEXbjnuTk41+rfwwutDAtfcYYpnqqfMiSVCj0VByEC8qM+AO+gBp6oxmpSaMPAiLuel3LCOxALYO7xkA86UtMpJBuETIrfhN1C01qpDTVQ6honuLKPSikjrCd7buyRtwBznPnGlW32AQySJTXKruNU7JFTHZ6UccpYAAr57447aNpcaYUTSfHSNPUSAVBK4b5G4Hvn7Y08/gn0zD1B1LV9Q3OnZKK0jatOAZHknb8ucdgBz98aMqBbJEJc2fpGxQdG2Chs4lkqquJTJUzLEW3zHlj2/QfQaKQ16TxLOlPVTiTBViuwAfY86nkvbpHiGguIGcKrIFI+uS2hEF2rqr4lv2XUU6RvtIndQGXPcEZBP210iFIZKgSB0oGUEnPqVKrkfpnUktLdap/3NPb4F3DG+d2z98DQ39py5YpQq2MKzlyQPvgaN2moqpAJZZ4yCfyojcD65106KXXj1z01Dap0tlxBbL000TNuZiAhVSccHJzg9tMVoY2ynpkpo1Cqg/dyIHUH2B4x9NJtZMa7r+419DDT1FTRyR0bzknCNjlF/wDL5u/jWkSW+OauRZVD+lGAQ3yleORx5/poAVrJMlRRJhJ6uSWlDV6mUnupUD/Y1n/VMUEqFfTG3GCgcnd5xj2++nd7UNpENMzk8qJpyQM+ecnS5ebWsSMsxiSVvC9v05yfvowJHMKsyJaOnt1wpvi5QgnUuf4Tjx9vto89Q6jdG0aoUxEgGFA9z/nqndrVFWApIhUR4MbsvzMBn+n99VKBaiUCSrLDawXavc8Z/wB+2lcm5hgKniUVMlHiEM0QJ/ePxuJPLY/t9tCpaF7dH6shyX4OO+O+jklymqaiWFo9qJtVVx+XXr4B6g4qOGYksPHHjQh85aAConqbcC/yLVj1BngqwI/ocaMW2ghTqG8VZ/OtPEgXHYDIP9dVP2NKHheJirgo5XP5hnXtLgUrJyO7RsjDycHOrDaQd59uTpsIkbK7NqkjySdZzere1n6ooa2AbIqhNjAHjcQcZH30yXi5yrcqeCJcxOPVI99vcY1U6lkiuVvUxOC8S9/KkHI1wNSpEG3Pp+03pFW82yjuLKMPLPADJ99455586o01g6doK1o6Tp+gSpiX9y8kIckY45bORx3+mitNXLOpWX5HaMBsA9/950t9R3BqCejq0y6xEo5HfaTzn+ef564O/FwZxrd1Ge6GWYUbxu3op8qxg8Be4A9gPA0NvFOae4WW5wjkSPTyAdnVgD/PjX2irmrYAiH867c+x8Efrg6MPi5dPUssZBaOZZM45BUnI1W/eTXtD9ZXC3271VUSblC5z79jofUSkmJYnyh5Ct4/3yMa90ymSDZKA8DJ4Hbn+3GqSwS0u71WDwF8xseRyeM+3Yc/TQzvL1UZumKcVFxjMwZI1YHcCSCD47ca1CeNIYVjVRMjR7flUhj+n/rSv0FanmCSyRbiw8Pt9+PsRrQntVUo9SCCNCndXdSf0OnMKkL9YJ6uLVH0/wBKRXOK+z9PUj3KkH7ud0JIcdtoPyg/XHGsW6siudR13dOor7NMsYqg1U0FT6bSRKuY0BHKRLnnydbpeq+W3Uc8lwYLTKwMpZN3y+QNZX1F1FaeorrVUlsoHrbVWwpEWdHQuw/KzjuFzx/fSWYsr6b28zAyt2yQfMS6q6T9X2iG79KXqKovjSvA6SoGho03ckq3nHZjkkaXj0/bbpUyzyVcd6qYJViqtqimo/UJ7E43O3P5RjOtKavoeiovg6qzpQ3Cn2JSUbqFhkkxkLwCNvu5J8Z196ghudILYzW9Kj1Ssq09Jgwwy/mLk4+b3DHA441AOkEoIgBTVFmelq6KroqesgSktabfRkp4/S7A5jdR4HcHUnUNPbFcVn72RpnjRmVy8cDfwS7fb38Ed9N98va1aK5hFTWkboo5ZFi7+GHJ7Z5HvpfFqe500Sww/CNUhoHpVYhXzwyK+ODjlc4+YaR1vro8GTbDgyrcblWUPVkCrMPnnh9SIyACRGVQ3HgjO4Y8ZGv1fSwXeWYW2dqStoJyZJIwAytk5Dr5DDv4Og1/olW/UAlrqaWd0p46dZ1HqCSL5Wb3yflz+up71QXf/mu8VtueKHfKQmWCFOBuGc4ZG74PIOhdRhQ+teZXYRn6ZeTqWKop4d9slpk2TrCoJRAcAqTxyQOfvpgnvNLRzU7i6VNLVSxNmaFdqOoIR0Lcc5Occ50ApqC4JaqWoo/TeqfDF4DuRmxgu4/iAHGD2Jz41JVR0FQvwFZGlS8kwngjEYl2OxAcFzwOcHjwNXwrYo+YxjyEEVCE1/mqKUR0Msa1GFeY/CJvaT8qHd3zgd9DmjkQlWFQSpwSKfg6WY7jdbleHp6GgjhMdT6TyO+0RKGxgnwe/fTZJUWFJGUvypIOPVI1Y4CW5/WWzZXU0d5mH/LslFXUz3WdaKlmiLyGN0qWA29+c5OOPponZZI0qXe1wSUipAFj9VGdpCozuI8ZBzxqwLHWXijnqq57FSwKhamhmmELSAcBVGeTx2PfX6XqWCCyPTUstdSXKKBqc1gQNHOh7qCc5AAx769A76lIAubzUF2FyGW/SoJ6O7XCq+KaYGKWJwI87cqpXwe/J7aIR26cXRLReGQOYBNI8tQMx5GQ+4cnPPJwNKdmoZHqErFqIUlqs4kljLseMAbD+XOANfKt7hWTtVK7VVxZfQmleVQmzygAB+310PtsDpU0IvpcGloCW+obZS28UBgn3tIhWRZJQymUtgbX77cYPPOujelbh0r+GvSqW4X2GeeRvXq2p1MrO57gBe/bHOsp/BX8OU6j6kN46vipkt1pYOKWVwQZsfu129jgHcf01utfY7PRVcs9JDCpZSQquWRTjwvYaYpggBN/OHQaR6t4sx/itaKuBpaajub1BbCQLTly48MMeMfy0XprjWF/iTaLhHIyKoaUiMH2+Unjv3xr6XtMDQyx0+ZVUYKRkBR9gMDUFRWyOx9G3VbjdlAQFLDPcE6iX28QvT1N1ra5Vgtish/NmrUYPvxovcKu59O0MtTV01KaYLuLRyEnPsR/npfgrbiiu0ND6R3ARl5V89zxqpcI6iruVu/5ojaSmim9SRKWVnJjH5mbAAwRkfroWRtKEiUc6VJEu9JPS2+usVKp9GW7VElbIkigsGIyc/4h9e+m++SwTVrbdyk8OUb83Pfg40mWZaG//jBJVdPRzJZrVb8x71PyF+Mc5xj66bLyI0ry9O2eMSEoCMfyGjMNIAk4+Jeo6ZEi201MIz3ZmkOfuTyc6rV3ooriKNHc95GHAx9e50MgS41MjLbpIXg537ojxjxwTjRGls1xndTIECeWdMcf+KeB9ToBDExhSBM/u1CSzPEGqJHJLSMcZ9hj20Gjo5YaWSOUZkO0bsYA8sP9fpxrULvS01upZZ6iXcUUt83P2+50hVTGa3lxlFRW3nv8zHJx7nnn21JWhvDA6pUttrERYVGXMzmV3bu7McfoAFHA1ZeJY6dpC2GZjtB9/B/XSzQ9Tt+1Km31SFRAiMHC+7c/bvo1StFXVFGJ5Bt2Akf+JyB+ulyROCkbGWjSpU+m0L/MB2zjGRn++qNbY4oxmcKXcbkce/tq/VTU1FtSAtvKjgDODgZ/qT/LUVe7VNIp3hWC7iVHH3/nrroSKmdVtOVuUbMoJQsiyD+39tLV6RoK932bYqhirAdgSdPNUAsz+oqs3psWA9wef8j+ulS9ufTDnGDKE2t57Y/01S5eoDqJHRS6Eb1jIOPPsdULvDvss0rg5EfHvydG0o/iKgGIn08Dbn/D3/XGo7lTbrdWAgengOSPHIH9f8tdcoRJen4EEcbKMjarLonbaYUBraFmLRgiZAT/AAtnI/poH03WRPbOSTLGHUDueOP8tNK03qxip9QCaNMY8Fdvb++hk+JYCe6WGcGWOD5mCMkaHycfLr3bV/a1XS+krRoQC6N4z3U/YjXukqmidGfPqBEdGx2Ze/8ATB0yUNJ6lXJU0IjEu75lYfn+n0P11KnxII3mhdOUr0kcfoyOqrgbdoIH205iSaSILGwcgZ+WMZxpBtt8FuT0KylkpXYceqP3T/8Axft/PtojT9RVks/oxWsBDwJWYg/pjg60EahUWYbwN+Jwll+AURMyoS7bCVKkeeTz9saxCuXq2hvTU1IJYaKqAD1QQL6qE5AD9yR4HYc66YubU8lqSoq3aRFJWQSbSuT29tZvcbrR09aKCklpa1lxItMZiwVsZ4I5GeBjtpDqFAfWaP1iebHtYAMVuounrNfIqSp6rNRWT0UPp1NJS1IUSKTgGRu4GcZHHfnQd666TQ/s24UAt1uUbaaCOo/7S4+VWKk5Xj/PR2pbpyzTftCJKkV0rOUaAfu23fnjY+CCeQeex1Uvk0M00Ijpkp7gadZI4qh9sMuR8x9Qdj/Y50sX1NXiZRVVFERPFio6+SIx00oWmyWiech2wcsFlHzA+QG/Q6ZeoNz2OJUrHgC7Gp5HdwwbxuPnxyc8jQ+S+xwTGO4U4pa1VGVUbh7DPlh9Rq+1fTx26spqinFTTOu6WAn5oQfzFD2wfzD6g6hlVhZ8Qbs1bbCAq2ohvt4pq670kM9Zb3eV5guBvCZ3gfXA/XQK+1FddOqp6anmkUSOiSDIKt8ozx4OMnOriGCy1NXRTzNVxRKEBI3M6kDnPAzg6+T3cyNObdOI0rJQUb0gGKjyT49tKdzlTAgkQrPe7yJI7dZ4nlpI3UCQd2A4HbwPOe/OmO2yVEjMtRU0tvRGVhLCQ3pMOdsgHb3B40DorJWtClRc6401BTrmWFdwlJzhcnzuJGMd9fRLV2mtt8dxniFsiY/E00aIqnngSHJkY4Pf3GrqKIvmHTEw/EaEL1VFbrXOfTrqB6WHNS52szOzZxJux82Tnk6z6XqK4+q/pz0JTcdpIGcfy1oVPdOjLJbIIoKgXGGqdhF6yO65zyGbuq59h31VaOyFjva0I2eV3y8H2/7WmSpO5P5ye0WJkdk6VsNwiqbvTTXiShgjFRJQCeP1FAw2UOQPr27fXSdeOqrVeK0yWyikVpJC6FpTsCDgD6v3yT76E9VTOt6q7PTk01HT1CBfS4dtxGSWPJ7/AG1+nm9OaqggihgioajbEEjAJ4HLHux+51uYwNWr3m2gayxO0bbB+yrhdaeivDQR25ArVby7ljRcZ2h/c9sY+upr7frVcLk1F01/9Ht7yKKKAU/q+rISBtBz7kffSDJPNFIYvWkeNZACGc4YYzyB351oP4adMUF16+sEVUJQqyPMNkhU7kQkc+2fGrOoG8KDOiejuk6DoTpmKgqJ0lqpJzNW1EoCepOQCcDOABjAH00QuN1pwu9qqBUUZ3Fh/lonV2mhi+GC0sR9WPe25Q3zEnkZ0FrcfFlAqKqpgAIP9NVDalBEoN5RlvdspikUtQ01RKuRtjJ49+Ow19qLnT0vok0VVVGQDc8SZCD9Tr0JpGckyNgKMKOANXaOmSorRFIWKE+/01VmIFzrC7yxB1dRWOMNHbpqullGKuKBFeUR+4BIOR340CvVLTVnVdKjzStZZFMz3RcwrTU+3eFz5LcKfroj1rXJ0901U1dBQULTxsFVpoBJjJwTz50hdNT1N0rKiCsq53padd0dOGAj5OfygdtZTX1DArtfP2/eKFhm9QsR6/C2jjrrp1HfUhEK1tQIaSWQEO8SDGSOx588Z0Uv9vqBUemK1HDHnKcKP0Iz/PRf8LaKnisVNJHCitK0rtgfxbzqr1rl2qFB2BCcbQBrXbiOptsIHtMlTa3EbXNDQqC2PT2N/LwM/wDrV25de0triPr1KhFGSAMkfpnk/fSzRYSmp6eNERZiWkfbliR9TnSX1dFEatYxEixh8bAODnyfc6ExuFUC450NbH1/dQ9RHPVUFMd8dIDtWV/DSv8A4R32judXerFittHIZFhMrLiONRgFvCqPb3OmDp+litfTdClCgiE+0uR3ORoHUxrX3yRapQ6wI7oMdivb9POqigYwpveZtFSyRRV81YgFS8POFxk45/z0rWWsrYS/5i/5F3+2TgY+nGtSrKWNqmtdgSVVVGfrydJlzVaaUSRKN21iM+OB2/npdhvCsbNwrbKYTGCqmYux3MwJzu8/+9e66ojWkiLNtZWKd/r3/Uf1GglNWSxbWTAOdnbsNuca+PUSS0UbyEM0dSVU48bc86ofaVqQT0LGtSSN90UqFV5/KSMY/mMaULsBUqyNgqJAJADjDKf6acqtytQgXCg4JwMc4B0nXqBBXVjDIZpNxOfOD/pqktB1bVmieKIk/kw7L2HntrxHPvpCJydgjVic/mLNx/LVeU+o9QH+YKjnnzhtTNEvr+lz6YKpjPjUSIPoKY22czk4hmLKAPGedNlgqJUbbL86GTcueQQuNe7RRQSzywyIGjj3bQfuP9TonbaCCGVkReCD57d/9NQ0iG/hd9NGioCN20N3I8Y/lxpn6QoxHukflGbJYDO09ske3HOhNC3rSQRPjY5+YDz20/2G3w0qRPCGUlip54Izo6Le8EzVGa3+hW0ZWRIyp4bGGGft50CvVup6GmL0Mho5IzvxB+Rh77fH3Gp7lbY6Knq6qkklhcKX2q3ykgcZGgkVW7wRE4DuoJYE57/f66b4EX5hiSAV3ToiqwrtKeW35yCPI+uke69N2qONDOIqYDAJdNjNjtjHOdOtVElf07LTzriN48HYSp7++kiLpWmMgmaqrXdIt67p84P8tLZDY3Egre0TL1eLda5K2iamutVDWsZJTLS/u5hjBJ2/NkY4Yc9tV6a2UVZ0tTxUcdVOoq2mohKQvw/Hzo7Y/K2cj66ZuqqJo6CpuslZVz1FGA8KyTHYGPGSBjP2PGkr4b4jqGy3Iz1CT1LOkypKRG6iMnaU7YJAyP8APSnaPAMys2Eg1cXo6arsZEvUFNTU9JTuY6OdpxJLKSeBFnsv+LPntpfqL1WmseSSFkjeb5YY+YjkYyp78nuD76fLrAl4apkrBkN6Q9MfkGV8A9seMY+udIvScAqOsaOjnZpKZKoSCNjxkfNj7ZGgne9vEUrULM9XyqW03SShpgs0VvhVcOMq8o/7mffk4/TQSkqnasjebCRUeAU3EbwTz+mqNfWST1V0d8bi28kDuW5P9dfbPmpr4hIxxLOA4HkY7aVx4xqMXAM3Xp3qqGrsF0uFW4qjb54XMDRNgjawiUY7gPyc/wCHSPcLyb4Zqu2yPE78gbMKCDzyBlW+4/XRKnpoqKy1NNSp6ULTqjKpK7gWyMkex7e2qt0tJopGalr66N41JDetkn5exJHP66d03Ge5ew8RTqrzcDIaGuiOUULHNPEUAkPJ+cY+U8c+NFFiuqqqmrmUgYIEinH640Ke+3SiNWI7jUyRuyq0Usm9CPbaeNVT1zes/wD8lB9oVH+WjBFPM6iRP//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='German_Shepherd.jpg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n02106662', 'German_shepherd', 0.9796372),\n",
       "  ('n02105162', 'malinois', 0.020184083),\n",
       "  ('n02091467', 'Norwegian_elkhound', 0.00015799515),\n",
       "  ('n02116738', 'African_hunting_dog', 5.2901587e-06),\n",
       "  ('n02105251', 'briard', 3.9127376e-06)]]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_image = prepare_image('German_Shepherd.jpg')\n",
    "predictions = mobile.predict(preprocessed_image)\n",
    "results = imagenet_utils.decode_predictions(predictions)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='labrador1.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n02099712', 'Labrador_retriever', 0.73073703),\n",
       "  ('n02087394', 'Rhodesian_ridgeback', 0.03984367),\n",
       "  ('n02092339', 'Weimaraner', 0.03359009),\n",
       "  ('n02109047', 'Great_Dane', 0.028944707),\n",
       "  ('n02110341', 'dalmatian', 0.022403581)]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_image = prepare_image('labrador1.jpg')\n",
    "predictions = mobile.predict(preprocessed_image)\n",
    "results = imagenet_utils.decode_predictions(predictions)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='poodle1.jpg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n02113799', 'standard_poodle', 0.5650911),\n",
       "  ('n02113712', 'miniature_poodle', 0.37279922),\n",
       "  ('n02102973', 'Irish_water_spaniel', 0.053150617),\n",
       "  ('n02113624', 'toy_poodle', 0.0072146286),\n",
       "  ('n02093859', 'Kerry_blue_terrier', 0.0013652634)]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_image = prepare_image('poodle1.jpg')\n",
    "predictions = mobile.predict(preprocessed_image)\n",
    "results = imagenet_utils.decode_predictions(predictions)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far so good. But lets try it on a type of bird, the blue tit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='blue_tit.jpg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[('n01592084', 'chickadee', 0.95554715),\n",
       "  ('n01530575', 'brambling', 0.012973112),\n",
       "  ('n01828970', 'bee_eater', 0.012916375),\n",
       "  ('n01532829', 'house_finch', 0.010978725),\n",
       "  ('n01580077', 'jay', 0.0020677084)]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed_image = prepare_image('blue_tit.jpg')\n",
    "predictions = mobile.predict(preprocessed_image)\n",
    "results = imagenet_utils.decode_predictions(predictions)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see it could not recognise the blue tit. It mistaklenly classified the image as a chickadee. This is a native bird to North America, and is subtely different: \n",
    "\n",
    "<img src=\"files/220px-Poecile-atricapilla-001.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now manipulate Mobilenetm the top few layers and employ transfer learning. To do this, we need to train it on some images. Here I will train it on Blue tits and Crows. But rather than manually downloading images of them, lets use Google Image Search and pull the images. To do this, there is a nice package we can import.\n",
    "\n",
    "Check out https://github.com/hardikvasa/google-images-download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install google_images_download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google_images_download import google_images_download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = google_images_download.googleimagesdownload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arguments = {\"keywords\":\"blue tit\",\"limit\":100,\"print_urls\":False,\"format\":\"jpg\", \"size\":\">400*300\"} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = response.download(arguments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arguments = {\"keywords\":\"crow\",\"limit\":100,\"print_urls\":False, \"format\":\"jpg\", \"size\":\">400*300\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "paths = response.download(arguments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets now use MobileNet as it is quite lightweight (17Mb), freeze the base layers and lets add and train the top few layers. Note only two classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ferhat\\Anaconda3\\lib\\site-packages\\keras_applications\\mobilenet.py:207: UserWarning: MobileNet shape is undefined. Weights for input shape (224, 224) will be loaded.\n",
      "  warnings.warn('MobileNet shape is undefined.'\n"
     ]
    }
   ],
   "source": [
    "base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.\n",
    "\n",
    "x=base_model.output\n",
    "x=GlobalAveragePooling2D()(x)\n",
    "x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.\n",
    "x=Dense(1024,activation='relu')(x) #dense layer 2\n",
    "x=Dense(512,activation='relu')(x) #dense layer 3\n",
    "preds=Dense(2,activation='softmax')(x) #final layer with softmax activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Model(inputs=base_model.input,outputs=preds)\n",
    "#specify the inputs\n",
    "#specify the outputs\n",
    "#now a model has been created based on our architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check the model architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 input_2\n",
      "1 conv1_pad\n",
      "2 conv1\n",
      "3 conv1_bn\n",
      "4 conv1_relu\n",
      "5 conv_dw_1\n",
      "6 conv_dw_1_bn\n",
      "7 conv_dw_1_relu\n",
      "8 conv_pw_1\n",
      "9 conv_pw_1_bn\n",
      "10 conv_pw_1_relu\n",
      "11 conv_pad_2\n",
      "12 conv_dw_2\n",
      "13 conv_dw_2_bn\n",
      "14 conv_dw_2_relu\n",
      "15 conv_pw_2\n",
      "16 conv_pw_2_bn\n",
      "17 conv_pw_2_relu\n",
      "18 conv_dw_3\n",
      "19 conv_dw_3_bn\n",
      "20 conv_dw_3_relu\n",
      "21 conv_pw_3\n",
      "22 conv_pw_3_bn\n",
      "23 conv_pw_3_relu\n",
      "24 conv_pad_4\n",
      "25 conv_dw_4\n",
      "26 conv_dw_4_bn\n",
      "27 conv_dw_4_relu\n",
      "28 conv_pw_4\n",
      "29 conv_pw_4_bn\n",
      "30 conv_pw_4_relu\n",
      "31 conv_dw_5\n",
      "32 conv_dw_5_bn\n",
      "33 conv_dw_5_relu\n",
      "34 conv_pw_5\n",
      "35 conv_pw_5_bn\n",
      "36 conv_pw_5_relu\n",
      "37 conv_pad_6\n",
      "38 conv_dw_6\n",
      "39 conv_dw_6_bn\n",
      "40 conv_dw_6_relu\n",
      "41 conv_pw_6\n",
      "42 conv_pw_6_bn\n",
      "43 conv_pw_6_relu\n",
      "44 conv_dw_7\n",
      "45 conv_dw_7_bn\n",
      "46 conv_dw_7_relu\n",
      "47 conv_pw_7\n",
      "48 conv_pw_7_bn\n",
      "49 conv_pw_7_relu\n",
      "50 conv_dw_8\n",
      "51 conv_dw_8_bn\n",
      "52 conv_dw_8_relu\n",
      "53 conv_pw_8\n",
      "54 conv_pw_8_bn\n",
      "55 conv_pw_8_relu\n",
      "56 conv_dw_9\n",
      "57 conv_dw_9_bn\n",
      "58 conv_dw_9_relu\n",
      "59 conv_pw_9\n",
      "60 conv_pw_9_bn\n",
      "61 conv_pw_9_relu\n",
      "62 conv_dw_10\n",
      "63 conv_dw_10_bn\n",
      "64 conv_dw_10_relu\n",
      "65 conv_pw_10\n",
      "66 conv_pw_10_bn\n",
      "67 conv_pw_10_relu\n",
      "68 conv_dw_11\n",
      "69 conv_dw_11_bn\n",
      "70 conv_dw_11_relu\n",
      "71 conv_pw_11\n",
      "72 conv_pw_11_bn\n",
      "73 conv_pw_11_relu\n",
      "74 conv_pad_12\n",
      "75 conv_dw_12\n",
      "76 conv_dw_12_bn\n",
      "77 conv_dw_12_relu\n",
      "78 conv_pw_12\n",
      "79 conv_pw_12_bn\n",
      "80 conv_pw_12_relu\n",
      "81 conv_dw_13\n",
      "82 conv_dw_13_bn\n",
      "83 conv_dw_13_relu\n",
      "84 conv_pw_13\n",
      "85 conv_pw_13_bn\n",
      "86 conv_pw_13_relu\n",
      "87 global_average_pooling2d_2\n",
      "88 dense_1\n",
      "89 dense_2\n",
      "90 dense_3\n",
      "91 dense_4\n"
     ]
    }
   ],
   "source": [
    "for i,layer in enumerate(model.layers):\n",
    "  print(i,layer.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use pre-trained weights as the model has been trained already on the Imagenet dataset. We ensure all the weights are non-trainable. We will only train the last few dense layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model.layers:\n",
    "    layer.trainable=False\n",
    "# or if we want to set the first 20 layers of the network to be non-trainable\n",
    "for layer in model.layers[:20]:\n",
    "    layer.trainable=False\n",
    "for layer in model.layers[20:]:\n",
    "    layer.trainable=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets load the training data into the ImageDataGenerator. Specify path, and it automatically sends the data for training in batches, simplifying the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 188 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) #included in our dependencies\n",
    "\n",
    "train_generator=train_datagen.flow_from_directory('C:/Users/Ferhat/Python Code/Workshop/Tensoorflow transfer learning/downloads',\n",
    "                                                 target_size=(224,224),\n",
    "                                                 color_mode='rgb',\n",
    "                                                 batch_size=32,\n",
    "                                                 class_mode='categorical',\n",
    "                                                 shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile the model. Now lets train it. Should take less than two minutes on a GTX1070 GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "5/5 [==============================] - 5s 952ms/step - loss: 0.9098 - acc: 0.6562\n",
      "Epoch 2/10\n",
      "5/5 [==============================] - 3s 563ms/step - loss: 0.0503 - acc: 0.9686\n",
      "Epoch 3/10\n",
      "5/5 [==============================] - 3s 687ms/step - loss: 0.0236 - acc: 0.9930\n",
      "Epoch 4/10\n",
      "5/5 [==============================] - 4s 716ms/step - loss: 7.5358e-04 - acc: 1.0000\n",
      "Epoch 5/10\n",
      "5/5 [==============================] - 3s 522ms/step - loss: 0.0021 - acc: 1.0000\n",
      "Epoch 6/10\n",
      "5/5 [==============================] - 4s 780ms/step - loss: 0.0353 - acc: 0.9937\n",
      "Epoch 7/10\n",
      "5/5 [==============================] - 3s 654ms/step - loss: 0.0905 - acc: 0.9938\n",
      "Epoch 8/10\n",
      "5/5 [==============================] - 4s 890ms/step - loss: 0.0047 - acc: 1.0000\n",
      "Epoch 9/10\n",
      "5/5 [==============================] - 3s 649ms/step - loss: 0.0377 - acc: 0.9867\n",
      "Epoch 10/10\n",
      "5/5 [==============================] - 5s 929ms/step - loss: 0.0125 - acc: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b156ca0e10>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "# Adam optimizer\n",
    "# loss function will be categorical cross entropy\n",
    "# evaluation metric will be accuracy\n",
    "\n",
    "step_size_train=train_generator.n//train_generator.batch_size\n",
    "model.fit_generator(generator=train_generator,\n",
    "                   steps_per_epoch=step_size_train,\n",
    "                   epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model is now trained. Now lets test some independent input images to check the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image(img_path, show=False):\n",
    "\n",
    "    img = image.load_img(img_path, target_size=(150, 150))\n",
    "    img_tensor = image.img_to_array(img)                    # (height, width, channels)\n",
    "    img_tensor = np.expand_dims(img_tensor, axis=0)         # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)\n",
    "    img_tensor /= 255.                                      # imshow expects values in the range [0, 1]\n",
    "\n",
    "    if show:\n",
    "        plt.imshow(img_tensor[0])                           \n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "\n",
    "    return img_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#img_path = 'C:/Users/Ferhat/Python Code/Workshop/Tensoorflow transfer learning/blue_tit.jpg'\n",
    "img_path = 'C:/Users/Ferhat/Python Code/Workshop/Tensoorflow transfer learning/crow.jpg'\n",
    "new_image = load_image(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(new_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.5191143e-15, 1.0000000e+00]], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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